Stem regress$ (all stems)

448 papers:

CASE-2015-SterlingSZC #algorithm #optimisation #parametricity #process

Welding parameter optimization based on Gaussian process regression Bayesian optimization algorithm (DS, TS, YZ, HC), pp. 1490–1496.

ICSME-2015-EkelundE #evaluation #industrial #performance #testing

Efficient regression testing based on test history: An industrial evaluation (EDE, EE), pp. 449–457.

SANER-2015-BezemerPG #comprehension #difference #graph #performance #using

Understanding software performance regressions using differential flame graphs (CPB, JP, BG), pp. 535–539.

SCAM-2015-GhanavatiA #automation #memory management #testing

Automated memory leak diagnosis by regression testing (MG, AA), pp. 191–200.

ICEIS-v2-2015-SariK #analysis #debugging #monitoring #predict

Bug Prediction for an ATM Monitoring Software — Use of Logistic Regression Analysis for Bug Prediction (ÖS, OK), pp. 382–387.

ICEIS-v2-2015-ThommazoCHGPBF #complexity #dependence #requirements #testing #using

Using the Dependence Level Among Requirements to Priorize the Regression Testing Set and Characterize the Complexity of Requirements Change (ADT, KC, EMH, GG, JP, AB, SF), pp. 231–241.

ICML-2015-HoangHL #big data #framework #modelling #probability #process

A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data (TNH, QMH, BKHL), pp. 569–578.

ICML-2015-KuklianskyS #linear #performance

Attribute Efficient Linear Regression with Distribution-Dependent Sampling (DK, OS), pp. 153–161.

KDD-2015-ChaoHZ #analysis #kernel

Optimal Kernel Group Transformation for Exploratory Regression Analysis and Graphics (PC, QH, MZ), pp. 905–914.

KDD-2015-FlaxmanWS

Who Supported Obama in 2012?: Ecological Inference through Distribution Regression (SRF, YXW, AJS), pp. 289–298.

KDD-2015-MomtazpourZRSR #cyber-physical #invariant #using

Analyzing Invariants in Cyber-Physical Systems using Latent Factor Regression (MM, JZ, SR, RKS, NR), pp. 2009–2018.

MLDM-2015-KrasotkinaM15a #analysis #approach

A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis (OK, VM), pp. 425–437.

SEKE-2015-Hori0TO #comparison #image #testing #web

An Oracle based on Image Comparison for Regression Testing of Web Applications (AH, ST, HT, MO), pp. 639–645.

SEKE-2015-WangJC #similarity #testing

Similarity-based regression test case prioritization (RW, SJ, DC), pp. 358–363.

SIGIR-2015-LuccheseNOPTV #algorithm #documentation #named #performance #rank

QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees (CL, FMN, SO, RP, NT, RV), pp. 73–82.

ECMFA-J-2012-PuissantSM15 #automation #consistency #nondeterminism #using

Resolving model inconsistencies using automated regression planning (JPP, RVDS, TM), pp. 461–481.

SAC-2015-BlombergBR #evolution #robust

Evolving regression trees robust to missing data (LCB, RCB, DDR), pp. 102–109.

SAC-2015-ChaudhuriMG #network #predict #using

QoS prediction for network data traffic using hierarchical modified regularized least squares rough support vector regression (AC, SM, SKG), pp. 659–661.

ESEC-FSE-2015-ShiYGM #reduction #testing

Comparing and combining test-suite reduction and regression test selection (AS, TY, AG, DM), pp. 237–247.

ICSE-v1-2015-SahaZKP #approach #information retrieval #testing

An Information Retrieval Approach for Regression Test Prioritization Based on Program Changes (RKS, LZ, SK, DEP), pp. 268–279.

ICSE-v1-2015-TanR #automation #named

relifix: Automated Repair of Software Regressions (SHT, AR), pp. 471–482.

ICSE-v1-2015-TerragniCZ #concurrent #effectiveness #named #source code #testing

RECONTEST: Effective Regression Testing of Concurrent Programs (VT, SCC, CZ), pp. 246–256.

ICSE-v1-2015-YiYLZW #analysis #testing

A Synergistic Analysis Method for Explaining Failed Regression Tests (QY, ZY, JL, CZ, CW), pp. 257–267.

ICSE-v2-2015-FooJAHZF #automation #case study #detection #industrial #performance

An Industrial Case Study on the Automated Detection of Performance Regressions in Heterogeneous Environments (KCF, ZMJ, BA, AEH, YZ, PF), pp. 159–168.

SPLC-2015-ValovGC #comparison #empirical #performance #predict #variability

Empirical comparison of regression methods for variability-aware performance prediction (PV, JG, KC), pp. 186–190.

ISSTA-2015-EpitropakisYHB #empirical #evaluation #multi #performance #testing

Empirical evaluation of pareto efficient multi-objective regression test case prioritisation (MGE, SY, MH, EKB), pp. 234–245.

ISSTA-2015-GligoricEM #dependence #testing

Practical regression test selection with dynamic file dependencies (MG, LE, DM), pp. 211–222.

QoSA-2014-JohnsenLPH #dependence #graph #modelling #slicing #verification

Regression verification of AADL models through slicing of system dependence graphs (AJ, KL, PP, KH), pp. 103–112.

ASE-2014-FelsingGKRU #automation #verification

Automating regression verification (DF, SG, VK, PR, MU), pp. 349–360.

CASE-2014-MahlerKLSMKPWFAG #learning #process #using

Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (JM, SK, ML, SS, AM, BK, SP, JW, MF, PA, KYG), pp. 532–539.

DAC-2014-WangOC #optimisation #performance #polynomial #synthesis

Enabling Efficient Analog Synthesis by Coupling Sparse Regression and Polynomial Optimization (YW, MO, CC), p. 6.

MSR-2014-NguyenNHNF #automation #case study #identification #industrial #performance

An industrial case study of automatically identifying performance regression-causes (THDN, MN, AEH, MNN, PF), pp. 232–241.

CHI-2014-Oulasvirta #automation #human-computer #modelling

Automated nonlinear regression modeling for HCI (AO), pp. 3899–3902.

CIKM-2014-KangLSSK #coordination #distributed #probability

Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression (DK, WL, KS, LS, UK), pp. 1269–1278.

CIKM-2014-VinzamuriLR #learning

Active Learning based Survival Regression for Censored Data (BV, YL, CKR), pp. 241–250.

ECIR-2014-Aly #normalisation #parametricity #using

Score Normalization Using Logistic Regression with Expected Parameters (RA), pp. 579–584.

ICML-c1-2014-RabinovichB #topic

The Inverse Regression Topic Model (MR, DMB), pp. 199–207.

ICML-c2-2014-0001NKA #estimation #probability

GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare (AA, HN, SK, SA), pp. 1989–1997.

ICML-c2-2014-GregorDMBW #network

Deep AutoRegressive Networks (KG, ID, AM, CB, DW), pp. 1242–1250.

ICML-c2-2014-HsuS

Heavy-tailed regression with a generalized median-of-means (DH, SS), pp. 37–45.

ICML-c2-2014-KarninH #linear

Hard-Margin Active Linear Regression (ZSK, EH), pp. 883–891.

ICML-c2-2014-YangLR #linear

Elementary Estimators for High-Dimensional Linear Regression (EY, ACL, PDR), pp. 388–396.

ICML-c2-2014-YiCS #linear

Alternating Minimization for Mixed Linear Regression (XY, CC, SS), pp. 613–621.

ICPR-2014-GuerreroR #process

Circular Regression Based on Gaussian Processes (PG, JRdS), pp. 3672–3677.

ICPR-2014-HuDG #experience #learning #online #recognition #visual notation

Online Regression of Grandmother-Cell Responses with Visual Experience Learning for Face Recognition (JH, WD, JG), pp. 4606–4611.

ICPR-2014-LiYLMDWX #higher-order #multi

Multiple-Output Regression with High-Order Structure Information (CL, LY, QL, FM, WD, YW, JX), pp. 3868–3873.

ICPR-2014-NieJ #learning #linear #using

Feature Learning Using Bayesian Linear Regression Model (SN, QJ), pp. 1502–1507.

ICPR-2014-Nilsson #polynomial #using

Elastic Net Regularized Logistic Regression Using Cubic Majorization (MN), pp. 3446–3451.

ICPR-2014-OuyedA #classification #kernel

Feature Relevance for Kernel Logistic Regression and Application to Action Classification (OO, MSA), pp. 1325–1329.

ICPR-2014-RingLRE #assessment #using

A Two-Stage Regression Using Bioimpedance and Temperature for Hydration Assessment During Sports (MR, CL, MR, BE), pp. 4519–4524.

ICPR-2014-TabuchiTDIMKK #estimation #memory management #multi #people

Spatial People Density Estimation from Multiple Viewpoints by Memory Based Regression (YT, TT, DD, II, HM, TK, KK), pp. 2209–2214.

ICPR-2014-YangXWL #learning #realtime

Real-Time Tracking via Deformable Structure Regression Learning (XY, QX, SW, PL), pp. 2179–2184.

KDD-2014-SahooHL #kernel #multi #online

Online multiple kernel regression (DS, SCHH, BL), pp. 293–302.

KDD-2014-WangZQWD #multi #predict #risk management

Clinical risk prediction with multilinear sparse logistic regression (FW, PZ, BQ, XW, ID), pp. 145–154.

KDD-2014-ZhouT #graph #multi

Multi-task copula by sparse graph regression (TZ, DT), pp. 771–780.

KR-2014-KazemiBKNP #relational

Relational Logistic Regression (SMK, DB, KK, SN, DP).

MLDM-2014-SenkoD #multi

Multiple Regression Method Based on Unexpandable and Irreducible Convex Combinations (OVS, AD), pp. 43–57.

FSE-2014-ElbaumRP #development #integration #testing

Techniques for improving regression testing in continuous integration development environments (SGE, GR, JP), pp. 235–245.

ICSE-2014-HuangMSZ #analysis #performance #testing

Performance regression testing target prioritization via performance risk analysis (PH, XM, DS, YZ), pp. 60–71.

ICSE-2014-YuSR #automation #framework #named #testing

SimRT: an automated framework to support regression testing for data races (TY, WSa, GR), pp. 48–59.

CAV-2014-CernyHRRT #concurrent #synthesis

Regression-Free Synthesis for Concurrency (PC, TAH, AR, LR, TT), pp. 568–584.

CAV-2014-GligoricMSEM #distributed #testing

Regression Test Selection for Distributed Software Histories (MG, RM, RS, LE, DM), pp. 293–309.

ICST-2014-EngstromMRB #testing #visual notation

Supporting Regression Test Scoping with Visual Analytics (EE, MM, PR, MB), pp. 283–292.

ICTSS-2014-PalmieriCO #automation #testing

Cutting Time-to-Market by Adopting Automated Regression Testing in a Simulated Environment (MP, AC, AÖ), pp. 129–144.

ISSTA-2014-BohmeR #complexity #fault #named

CoREBench: studying complexity of regression errors (MB, AR), pp. 105–115.

ISSTA-2014-PastoreMHFSSM #testing

Verification-aided regression testing (FP, LM, AEJH, GF, NS, SS, AM), pp. 37–48.

ISSTA-2014-PradelHG #concurrent #performance #testing

Performance regression testing of concurrent classes (MP, MH, TRG), pp. 13–25.

ASE-2013-KukrejaHT #game studies #testing #using

Randomizing regression tests using game theory (NK, WGJH, MT), pp. 616–621.

ASE-2013-SagdeoEPV #automation #debugging #invariant #locality #testing #using

Using automatically generated invariants for regression testing and bug localization (PS, NE, DP, SV), pp. 634–639.

DAC-2013-LinLM #analysis #hybrid #kernel #reachability #verification

Verification of digitally-intensive analog circuits via kernel ridge regression and hybrid reachability analysis (HL, PL, CJM), p. 6.

DAC-2013-YinQ #security

Improving PUF security with regression-based distiller (CEDY, GQ), p. 6.

DATE-2013-QianJBTMM #analysis #named #performance #using

SVR-NoC: a performance analysis tool for network-on-chips using learning-based support vector regression model (ZQ, DCJ, PB, CYT, DM, RM), pp. 354–357.

CSMR-2013-GhaithWPM #analysis #detection #independence #performance #testing

Profile-Based, Load-Independent Anomaly Detection and Analysis in Performance Regression Testing of Software Systems (SG, MW, PP, JM), pp. 379–383.

ICSM-2013-MarijanGS #case study #industrial #testing

Test Case Prioritization for Continuous Regression Testing: An Industrial Case Study (DM, AG, SS), pp. 540–543.

ICSM-2013-SchwartzD #effectiveness #fuzzy #testing

A Fuzzy Expert System for Cost-Effective Regression Testing Strategies (AS, HD), pp. 1–10.

STOC-2013-ClarksonW #approximate #rank

Low rank approximation and regression in input sparsity time (KLC, DPW), pp. 81–90.

STOC-2013-MengM #linear #robust

Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression (XM, MWM), pp. 91–100.

HIMI-LCCB-2013-AntolosLLV #analysis #using

Burglary Crime Analysis Using Logistic Regression (DA, DL, AL, DAV), pp. 549–558.

ICML-c1-2013-ChenC13a

Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery (YC, CC), pp. 383–391.

ICML-c1-2013-GiguereLMS #algorithm #approach #bound #learning #predict

Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction (SG, FL, MM, KS), pp. 107–114.

ICML-c3-2013-BalasubramanianYL #learning

Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations (KB, KY, GL), pp. 289–297.

ICML-c3-2013-ChagantyL #linear

Spectral Experts for Estimating Mixtures of Linear Regressions (ATC, PL), pp. 1040–1048.

ICML-c3-2013-ChenCM #robust

Robust Sparse Regression under Adversarial Corruption (YC, CC, SM), pp. 774–782.

ICML-c3-2013-DuvenaudLGTG #composition #kernel #parametricity

Structure Discovery in Nonparametric Regression through Compositional Kernel Search (DKD, JRL, RBG, JBT, ZG), pp. 1166–1174.

ICML-c3-2013-GrosshansSBS #game studies #problem

Bayesian Games for Adversarial Regression Problems (MG, CS, MB, TS), pp. 55–63.

ICML-c3-2013-HockingRVB #detection #learning #using

Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression (TH, GR, JPV, FRB), pp. 172–180.

ICML-c3-2013-MengM #pipes and filters #robust

Robust Regression on MapReduce (XM, MWM), pp. 888–896.

ICML-c3-2013-OlivaPS

Distribution to Distribution Regression (JBO, BP, JGS), pp. 1049–1057.

ICML-c3-2013-ShenderL #trade-off

Computation-Risk Tradeoffs for Covariance-Thresholded Regression (DS, JDL), pp. 756–764.

ICML-c3-2013-YangMM #scalability

Quantile Regression for Large-scale Applications (JY, XM, MWM), pp. 881–887.

ICML-c3-2013-YangX #algorithm #robust

A Unified Robust Regression Model for Lasso-like Algorithms (WY, HX), pp. 585–593.

KDD-2013-CaiQ #analysis #linear #on the #rank

On the equivalent of low-rank linear regressions and linear discriminant analysis based regressions (XC, CHQD, FN, HH), pp. 1124–1132.

KDD-2013-ChenCMG

Density-based logistic regression (WC, YC, YM, BG), pp. 140–148.

KDD-2013-LozanoJD #distance #estimation #matrix #multi #robust

Robust sparse estimation of multiresponse regression and inverse covariance matrix via the L2 distance (ACL, HJ, XD), pp. 293–301.

KDD-2013-RistanoskiLB

A time-dependent enhanced support vector machine for time series regression (GR, WL, JB), pp. 946–954.

KDD-2013-ZhengM #optimisation #parallel

Optimizing parallel belief propagation in junction treesusing regression (LZ, OJM), pp. 757–765.

KDIR-KMIS-2013-PapulaVH #analysis #enterprise #network #process

Knowledge Networks as a Source of Knowledge Initiatives and Innovation Activity in Small and Medium Enterprises — Regression Analysis for EU 27 Countries (JP, JV, JH), pp. 389–396.

MLDM-2013-AmalamanER #detection #using

Using Turning Point Detection to Obtain Better Regression Trees (PKA, CFE, NJR), pp. 325–339.

SAC-2013-GholipourHB #adaptation #data type

An adaptive regression tree for non-stationary data streams (AG, MJH, HB), pp. 815–817.

SAC-2013-SeelandKP #graph #kernel #learning

Model selection based product kernel learning for regression on graphs (MS, SK, BP), pp. 136–143.

ESEC-FSE-2013-BeyerLNSW #performance #precise #reuse #verification

Precision reuse for efficient regression verification (DB, SL, EN, AS, PW), pp. 389–399.

ESEC-FSE-2013-BohmeOR #fault #interactive #testing

Regression tests to expose change interaction errors (MB, BCdSO, AR), pp. 334–344.

ICSE-2013-BohmeOR #verification

Partition-based regression verification (MB, BCdSO, AR), pp. 302–311.

ICSE-2013-PastoreMG #c #c++ #debugging #named #problem

RADAR: a tool for debugging regression problems in C/C++ software (FP, LM, AG), pp. 1335–1338.

ICSE-2013-SukkerdBWZB #comprehension

Understanding regression failures through test-passing and test-failing code changes (RS, IB, JW, SZ, YB), pp. 1177–1180.

ICSE-2013-SwearnginCJB #performance #testing

Human performance regression testing (AS, MBC, BEJ, RKEB), pp. 152–161.

ASPLOS-2013-OliveiraFDHS #why

Why you should care about quantile regression (ABdO, SF, AD, MH, PFS), pp. 207–218.

ICST-2013-Bauersfeld #named #testing #user interface #visual notation

GUIdiff — A Regression Testing Tool for Graphical User Interfaces (SB), pp. 499–500.

ICST-2013-YuQAR #testing

Oracle-based Regression Test Selection (TY, XQ, MA, GR), pp. 292–301.

ISSTA-2013-Ghaith #analysis #performance #testing #transaction

Analysis of performance regression testing data by transaction profiles (SG), pp. 370–373.

ASE-2012-HwangXKMT #evolution #policy #security #testing

Selection of regression system tests for security policy evolution (JH, TX, DEK, TM, YLT), pp. 266–269.

ASE-2012-YuLCZ #debugging #fault

Practical isolation of failure-inducing changes for debugging regression faults (KY, ML, JC, XZ), pp. 20–29.

DATE-2012-MaricauJG #analysis #learning #multi #reliability #using

Hierarchical analog circuit reliability analysis using multivariate nonlinear regression and active learning sample selection (EM, DdJ, GGEG), pp. 745–750.

VLDB-2012-ZhangZXYW #analysis #difference #functional #privacy

Functional Mechanism: Regression Analysis under Differential Privacy (JZ, ZZ, XX, YY, MW), pp. 1364–1375.

ICSM-2012-BeszedesGSJLG #test coverage #testing

Code coverage-based regression test selection and prioritization in WebKit (ÁB, TG, LS, JJ, LL, TG), pp. 46–55.

ICSM-2012-QuAR #impact analysis #testing #using

Configuration selection using code change impact analysis for regression testing (XQ, MA, BR), pp. 129–138.

ICSM-2012-RachatasumritK #empirical #refactoring #testing

An empirical investigation into the impact of refactoring on regression testing (NR, MK), pp. 357–366.

CIKM-2012-XuZG #multi #online

Multiview hierarchical bayesian regression model andapplication to online advertising (TX, RZ, ZG), pp. 485–494.

ICML-2012-BoukouvalasBC #process #using

Gaussian Process Quantile Regression using Expectation Propagation (AB, RB, DC), p. 123.

ICML-2012-GuL #parametricity

Sequential Nonparametric Regression (HG, JDL), p. 54.

ICML-2012-HannahD #design #geometry #programming

Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design (LH, DBD), p. 24.

ICML-2012-HazanK12a #linear

Linear Regression with Limited Observation (EH, TK), p. 242.

ICML-2012-LozanoS #multi

Multi-level Lasso for Sparse Multi-task Regression (ACL, GS), p. 80.

ICML-2012-PurushothamL #collaboration #matrix #recommendation #social #topic

Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems (SP, YL), p. 92.

ICML-2012-WilsonKG #network #process

Gaussian Process Regression Networks (AGW, DAK, ZG), p. 149.

ICML-2012-XuL #multi

Conditional Sparse Coding and Grouped Multivariate Regression (MX, JDL), p. 116.

ICML-2012-ZhouLDC

Lognormal and Gamma Mixed Negative Binomial Regression (MZ, LL, DBD, LC), p. 113.

ICPR-2012-BrownLG #linear #recognition

Locality-Regularized Linear Regression for face recognition (DB, HL, YG), pp. 1586–1589.

ICPR-2012-DuH #adaptation #approach #linear

A discriminative linear regression approach to OCR adaptation (JD, QH), pp. 629–632.

ICPR-2012-El-GaalyT #kernel #multi #recognition #using

RGBD object pose recognition using local-global multi-kernel regression (TEG, MT), pp. 2468–2471.

ICPR-2012-FiaschiKNH #learning

Learning to count with regression forest and structured labels (LF, UK, RN, FAH), pp. 2685–2688.

ICPR-2012-Gao12a #clustering #estimation #multi #using

Facial age estimation using Clustered Multi-task Support Vector Regression Machine (PXG), pp. 541–544.

ICPR-2012-GuiST #analysis #estimation #parametricity

Regularization parameter estimation for spectral regression discriminant analysis based on perturbation theory (JG, ZS, TT), pp. 401–404.

ICPR-2012-GuQFLW #image #kernel #multi

Image super-resolution based on multikernel regression (YG, YQ, TZF, CL, HW), pp. 2071–2074.

ICPR-2012-GuSM #image #performance

Fast image super resolution via local regression (SG, NS, FM), pp. 3128–3131.

ICPR-2012-GuyonBZ #constraints #detection #matrix #rank #robust

Foreground detection via robust low rank matrix factorization including spatial constraint with Iterative reweighted regression (CG, TB, EHZ), pp. 2805–2808.

ICPR-2012-HinoWM #slicing

Sliced inverse regression with conditional entropy minimization (HH, KW, NM), pp. 1185–1188.

ICPR-2012-LiLLL #distance #estimation #learning #metric

Learning distance metric regression for facial age estimation (CL, QL, JL, HL), pp. 2327–2330.

ICPR-2012-LiuDHZL #linear

A cost construction via MSW and linear regression for stereo matching (TL, XD, ZH, XZ, LL), pp. 914–917.

ICPR-2012-TurkovKM #concept #pattern matching #pattern recognition #problem #recognition

The Bayesian logistic regression in pattern recognition problems under concept drift (PAT, OK, VM), pp. 2976–2979.

ICPR-2012-ZhangWDP #detection #linear

Skin detection via linear regression tree (JZ, HW, FD, CP), pp. 1711–1714.

ICPR-2012-ZhengZ #kernel #recognition #speech

Speech emotion recognition based on kernel reduced-rank regression (WZ, XZ), pp. 1972–1976.

KDD-2012-DuivesteijnFK #distance #mining #modelling

Different slopes for different folks: mining for exceptional regression models with cook’s distance (WD, AF, AJK), pp. 868–876.

KDD-2012-LouCG #classification #modelling

Intelligible models for classification and regression (YL, RC, JG), pp. 150–158.

KDIR-2012-DubeyBP #named

BINGR: Binary Search based Gaussian Regression (HD, SB, VP), pp. 258–263.

KEOD-2012-KhairMZ #data mining #education #mining #roadmap #student #using

Creating an Educational Roadmap for Engineering Students via an Optimal and Iterative Yearly Regression Tree using Data Mining (MK, CEM, WZ), pp. 43–52.

MLDM-2012-BharambeDP #named #performance

BINER — BINary Search Based Efficient Regression (SB, HD, VP), pp. 76–85.

MLDM-2012-Thombre #classification #network

Comparing Logistic Regression, Neural Networks, C5.0 and M5′ Classification Techniques (AT), pp. 132–140.

SEKE-2012-NetoBSACR #fuzzy #testing

Regression Testing Prioritization Based on Fuzzy Inference Systems (PSN, RB, TS, WA, JC, RALR), pp. 273–278.

SEKE-2012-SunLTZ #impact analysis #testing #using

Using FCA-based Change Impact Analysis for Regression Testing (XS, BL, CT, QZ), pp. 452–457.

ECMFA-2012-PuissantSM #consistency #design #named #nondeterminism

Badger: A Regression Planner to Resolve Design Model Inconsistencies (JPP, RVDS, TM), pp. 146–161.

SAC-2012-SeelandBKP

Maximum Common Subgraph based locally weighted regression (MS, FB, SK, BP), pp. 165–172.

FSE-2012-ZhangKK #evolution #fault #impact analysis #java #named #source code

FaultTracer: a change impact and regression fault analysis tool for evolving Java programs (LZ, MK, SK), p. 40.

ICSE-2012-Bohme #clustering

Software regression as change of input partitioning (MB), pp. 1523–1526.

ICSE-2012-MarinescuC #execution #symbolic computation #testing

make test-zesti: A symbolic execution solution for improving regression testing (PDM, CC), pp. 716–726.

PLEASE-2012-LityLSG #modelling #testing

Delta-oriented model-based SPL regression testing (SL, ML, IS, UG), pp. 53–56.

SPLC-2012-HeiderRGL #impact analysis #modelling #testing #using #variability

Using regression testing to analyze the impact of changes to variability models on products (WH, RR, PG, DL), pp. 196–205.

ICST-2012-ChenLYS #question #testing #user interface #what

When a GUI Regression Test Failed, What Should be Blamed? (JC, ML, KY, BS), pp. 467–470.

ICST-2012-GuoSC #analysis #clustering #testing

Analysis of Test Clusters for Regression Testing (BG, MS, PC), p. 736.

ICST-2012-KauffmanK #framework #industrial #research #testing

A Framework to Support Research in and Encourage Industrial Adoption of Regression Testing Techniques (JMK, GMK), pp. 907–908.

ICST-2012-MarbackDE #approach #effectiveness #php #testing #web

An Effective Regression Testing Approach for PHP Web Applications (AM, HD, NE), pp. 221–230.

ICST-2012-Nguyen #comprehension #detection #performance #scalability #using

Using Control Charts for Detecting and Understanding Performance Regressions in Large Software (THDN), pp. 491–494.

ICST-2012-RunesonE #3d #problem #product line #testing

Software Product Line Testing — A 3D Regression Testing Problem (PR, EE), pp. 742–746.

ICST-2012-YuL #debugging #fault #towards

Towards Practical Debugging for Regression Faults (KY, ML), pp. 487–490.

ICTSS-2012-TaylorHBD #behaviour #testing #using

Using Behaviour Inference to Optimise Regression Test Sets (RT, MH, KB, JD), pp. 184–199.

ISSTA-2012-KimCHKOSPMN #performance #testing

Efficient regression testing of ontology-driven systems (MK, JC, MJH, TMK, AO, JHS, ARP, KM, SBN), pp. 320–330.

ISSTA-2012-ZhangMZK #mutation testing #testing

Regression mutation testing (LZ, DM, LZ, SK), pp. 331–341.

VMCAI-2012-ChakiGS #concurrent #multi #source code #thread #verification

Regression Verification for Multi-threaded Programs (SC, AG, OS), pp. 119–135.

ASE-2011-RobinsonEPAL #automation #generative #scalability #source code #testing

Scaling up automated test generation: Automatically generating maintainable regression unit tests for programs (BR, MDE, JHP, VA, NL), pp. 23–32.

CASE-2011-PurwinsNBHKLPW #predict

Regression methods for prediction of PECVD Silicon Nitride layer thickness (HP, AN, BB, UH, AK, BL, GP, KW), pp. 387–392.

FASE-2011-KhanH #analysis #contract #dependence #modelling #on the #testing #using #visual notation

On Model-Based Regression Testing of Web-Services Using Dependency Analysis of Visual Contracts (TAK, RH), pp. 341–355.

CSMR-2011-JurgensHDFSW #testing

Regression Test Selection of Manual System Tests in Practice (EJ, BH, FD, MF, CS, AW), pp. 309–312.

CSMR-2011-SalehieLTDLM #testing

Prioritizing Requirements-Based Regression Test Cases: A Goal-Driven Practice (MS, SL, LT, RD, SL, MM), pp. 329–332.

ICSM-2011-HuangLZXW #approach #novel #testing

A novel approach to regression test selection for J2EE applications (SH, ZJL, JZ, YX, WW), pp. 13–22.

ICSM-2011-KuhnK #combinator #detection #fault #testing

Practical combinatorial (t-way) methods for detecting complex faults in regression testing (RK, RK), p. 599.

ICSM-2011-RogstadBDRA #automation #case study #database #experience #industrial #legacy #testing

Industrial experiences with automated regression testing of a legacy database application (ER, LCB, RD, MR, EA), pp. 362–371.

ICSM-2011-SrikanthC #as a service #case study #industrial #testing

Regression testing in Software as a Service: An industrial case study (HS, MBC), pp. 372–381.

HCI-DDA-2011-ChangZDWH #multi #sketching #synthesis

Face Sketch Synthesis via Multivariate Output Regression (LC, MZ, XD, ZW, YH), pp. 555–561.

CIKM-2011-YooYC #classification #email #modelling #personalisation

Modeling personalized email prioritization: classification-based and regression-based approaches (SY, YY, JGC), pp. 729–738.

ICML-2011-BrouarddS #kernel #predict

Semi-supervised Penalized Output Kernel Regression for Link Prediction (CB, FdB, MS), pp. 593–600.

ICML-2011-IkonomovskaGZD

Speeding-Up Hoeffding-Based Regression Trees With Options (EI, JG, BZ, SD), pp. 537–544.

ICML-2011-Lazaro-GredillaT #process

Variational Heteroscedastic Gaussian Process Regression (MLG, MKT), pp. 841–848.

ICML-2011-MachartPARG #kernel #learning #probability #rank

Stochastic Low-Rank Kernel Learning for Regression (PM, TP, SA, LR, HG), pp. 969–976.

ICML-2011-MeyerBS #approach #constraints #linear

Linear Regression under Fixed-Rank Constraints: A Riemannian Approach (GM, SB, RS), pp. 545–552.

KDD-2011-MalbasaV #scalability

Spatially regularized logistic regression for disease mapping on large moving populations (VM, SV), pp. 1352–1360.

KDD-2011-YuanHL

An improved GLMNET for l1-regularized logistic regression (GXY, CHH, CJL), pp. 33–41.

RecSys-2011-ZhangAC #flexibility #matrix

Generalizing matrix factorization through flexible regression priors (LZ, DA, BCC), pp. 13–20.

SEKE-2011-Amasaki #case study #consistency #estimation #linear #nondeterminism #performance

A Study on Performance Inconsistency between Estimation by Analogy and Linear Regression (SA), pp. 485–488.

SEKE-2011-TaoLG #approach #component #modelling #testing

A Model-based Approach to Regression Testing of Component-based Software (CT, BL, JG), pp. 230–237.

SEKE-2011-YuLFC #composition #testing

A Regression Test Technique for Analyzing the Functionalities of Service Composition (HY, DL, GF, LC), pp. 578–582.

SIGIR-2011-WuYLLYX #learning #rank #using

Learning to rank using query-level regression (JW, ZY, YL, HL, ZY, KX), pp. 1091–1092.

ICST-2011-ChenCZXF #clustering #testing #using

Using semi-supervised clustering to improve regression test selection techniques (SC, ZC, ZZ, BX, YF), pp. 1–10.

ICST-2011-EngstromRL #case study #industrial #performance #testing

Improving Regression Testing Transparency and Efficiency with History-Based Prioritization — An Industrial Case Study (EE, PR, AL), pp. 367–376.

ICST-2011-NandaMSHO #testing

Regression testing in the presence of non-code changes (AN, SM, SS, MJH, AO), pp. 21–30.

ISSTA-2011-TanejaXTH #generative #named #performance #testing

eXpress: guided path exploration for efficient regression test generation (KT, TX, NT, JdH), pp. 1–11.

DAC-2010-ZhangCTL #modelling #multi #performance #scalability #towards

Toward efficient large-scale performance modeling of integrated circuits via multi-mode/multi-corner sparse regression (WZ, THC, MYT, XL), pp. 897–902.

DATE-2010-GanapathyCGR #estimation #modelling #multi #variability

Circuit propagation delay estimation through multivariate regression-based modeling under spatio-temporal variability (SG, RC, AG, AR), pp. 417–422.

ICSM-2010-LiQJW #automation #generative #graph #testing

Automatic test case selection and generation for regression testing of composite service based on extensible BPEL flow graph (BL, DQ, SJ, DW), pp. 1–10.

CIKM-2010-YuKN #named #question

RankSVR: can preference data help regression? (HY, SK, SHN), pp. 879–888.

ICML-2010-KimSD #algorithm #scalability

A scalable trust-region algorithm with application to mixed-norm regression (DK, SS, ISD), pp. 519–526.

ICML-2010-KimX #multi

Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity (SK, EPX), pp. 543–550.

ICML-2010-PardoeS

Boosting for Regression Transfer (DP, PS), pp. 863–870.

ICML-2010-TuL #classification #multi

One-sided Support Vector Regression for Multiclass Cost-sensitive Classification (HHT, HTL), pp. 1095–1102.

ICML-2010-YanQ #process

Sparse Gaussian Process Regression via L1 Penalization (FY, Y(Q), pp. 1183–1190.

ICPR-2010-AsheriRPR #adaptation #fault #framework #kernel #process

A Gaussian Process Regression Framework for Spatial Error Concealment with Adaptive Kernels (HA, HRR, NP, MHR), pp. 4541–4544.

ICPR-2010-GaoHLZW #detection #kernel

Local Outlier Detection Based on Kernel Regression (JG, WH, WL, Z(Z, OW), pp. 585–588.

ICPR-2010-GonenA #kernel #locality #multi

Localized Multiple Kernel Regression (MG, EA), pp. 1425–1428.

ICPR-2010-HiraiUK #performance #realtime

Real-Time Pose Regression with Fast Volume Descriptor Computation (MH, NU, MK), pp. 1852–1855.

ICPR-2010-KusakunniranWZL #multi #recognition #using

Multi-view Gait Recognition Based on Motion Regression Using Multilayer Perceptron (WK, QW, JZ, HL), pp. 2186–2189.

ICPR-2010-NaseemTB #recognition #robust

Robust Regression for Face Recognition (IN, RT, MB), pp. 1156–1159.

ICPR-2010-RudovicPP #multi #recognition

Regression-Based Multi-view Facial Expression Recognition (OR, IP, MP), pp. 4121–4124.

KDD-2010-Sculley #ranking

Combined regression and ranking (DS), pp. 979–988.

KDD-2010-WangLZ #analysis #approach #bibliography #rating

Latent aspect rating analysis on review text data: a rating regression approach (HW, YL, CZ), pp. 783–792.

KDD-2010-YangO #feature model #predict #probability #using

Feature selection for support vector regression using probabilistic prediction (JBY, CJO), pp. 343–352.

KDIR-2010-DesaiSP #named #performance #robust #scalability

SEAR — Scalable, Efficient, Accurate, Robust kNN-based Regression (AD, HS, VP), pp. 392–395.

SEKE-2010-DuanCZQY #clustering #slicing #testing

Improving Cluster Selection Techniques of Regression Testing by Slice Filtering (YD, ZC, ZZ, JQ, ZY), pp. 253–258.

SEKE-2010-FagundesS #estimation #fault #using

Software Defect Estimation using Support Vector Regression (RAdAF, RMCRdS), pp. 265–268.

SIGIR-2010-ChenYLZQ #classification #personalisation #predict #query #question

Predicting query potential for personalization, classification or regression? (CC, MY, SL, TZ, HQ), pp. 725–726.

SAC-2010-AppiceCM #learning

Transductive learning for spatial regression with co-training (AA, MC, DM), pp. 1065–1070.

SAC-2010-BaccianellaES #feature model

Feature selection for ordinal regression (SB, AE, FS), pp. 1748–1754.

SAC-2010-MaranzatoPLN #detection #using

Fraud detection in reputation systems in e-markets using logistic regression (RM, AMP, APdL, MN), pp. 1454–1455.

FSE-2010-JinOX #behaviour #named #testing

BERT: a tool for behavioral regression testing (WJ, AO, TX), pp. 361–362.

ICST-2010-Engstrom #product line #testing

Regression Test Selection and Product Line System Testing (EE), pp. 512–515.

ICST-2010-EngstromRW #empirical #evaluation #recommendation #testing

An Empirical Evaluation of Regression Testing Based on Fix-Cache Recommendations (EE, PR, GW), pp. 75–78.

ICST-2010-JinOX #automation #behaviour #testing

Automated Behavioral Regression Testing (WJ, AO, TX), pp. 137–146.

ICST-2010-NagahawatteD #effectiveness #fault #testing

The Effectiveness of Regression Testing Techniques in Reducing the Occurrence of Residual Defects (PN, HD), pp. 79–88.

ICST-2010-NaslavskyZR #modelling #named #testing #traceability

MbSRT2: Model-Based Selective Regression Testing with Traceability (LN, HZ, DJR), pp. 89–98.

ICST-2010-RoestMD #ajax #testing

Regression Testing Ajax Applications: Coping with Dynamism (DR, AM, AvD), pp. 127–136.

TAP-2010-GladischTBY #generative #testing #using #verification

Generating Regression Unit Tests Using a Combination of Verification and Capture & Replay (CG, SST, BB, AY), pp. 61–76.

DAC-2009-GodlinS #verification

Regression verification (BG, OS), pp. 466–471.

DAC-2009-Li #equation #modelling #performance #scalability

Finding deterministic solution from underdetermined equation: large-scale performance modeling by least angle regression (XL0), pp. 364–369.

ICSM-2009-Harrold #reuse #testing

Reduce, reuse, recycle, recover: Techniques for improved regression testing (MJH), p. 5.

ICSM-2009-NaslavskyZR #modelling #testing

A model-based regression test selection technique (LN, HZ, DJR), pp. 515–518.

ICSM-2009-YangDR #model checking

Regression model checking (GY, MBD, GR), pp. 115–124.

CIKM-2009-FeiH #graph #kernel

L2 norm regularized feature kernel regression for graph data (HF, JH), pp. 593–600.

ECIR-2009-LeaseAC #learning #query #rank

Regression Rank: Learning to Meet the Opportunity of Descriptive Queries (ML, JA, WBC), pp. 90–101.

ICML-2009-MooijJPS #dependence #modelling

Regression by dependence minimization and its application to causal inference in additive noise models (JMM, DJ, JP, BS), pp. 745–752.

ICML-2009-ZhuAX #classification #modelling #named #topic

MedLDA: maximum margin supervised topic models for regression and classification (JZ, AA, EPX), pp. 1257–1264.

KDD-2009-AgarwalC #modelling

Regression-based latent factor models (DA, BCC), pp. 19–28.

KDD-2009-LiuCY #scalability

Large-scale sparse logistic regression (JL, JC, JY), pp. 547–556.

MLDM-2009-RosenthalVHHL

Drift-Aware Ensemble Regression (FR, PBV, MH, DH, WL), pp. 221–235.

RecSys-2009-ParkC #recommendation

Pairwise preference regression for cold-start recommendation (STP, WC), pp. 21–28.

SAC-2009-HuangCZLT #java #testing

An optimized change-driven regression testing selection strategy for binary Java applications (SH, YC, JZ, ZJL, HT), pp. 558–565.

CAV-2009-Strichman #equivalence #proving #source code #verification

Regression Verification: Proving the Equivalence of Similar Programs (OS), p. 63.

ICST-2009-WikstrandFGZW #testing

Dynamic Regression Test Selection Based on a File Cache (GW, RF, JKG, WZ, CW), pp. 299–302.

ASE-2008-TanejaX #automation #generative #named #testing

DiffGen: Automated Regression Unit-Test Generation (KT, TX), pp. 407–410.

DAC-2008-LiL #modelling #performance #statistics

Statistical regression for efficient high-dimensional modeling of analog and mixed-signal performance variations (XL, HL), pp. 38–43.

DATE-2008-Liu08a #correlation #performance #random #simulation

Spatial Correlation Extraction via Random Field Simulation and Production Chip Performance Regression (BL), pp. 527–532.

ICSM-2008-HouZXS #testing

Quota-constrained test-case prioritization for regression testing of service-centric systems (SSH, LZ, TX, JS), pp. 257–266.

ICSM-2008-KorelKT #modelling #testing

Application of system models in regression test suite prioritization (BK, GK, LHT), pp. 247–256.

STOC-2008-DasK #algorithm #linear #set

Algorithms for subset selection in linear regression (AD, DK), pp. 45–54.

ICML-2008-CaronD #parametricity

Sparse Bayesian nonparametric regression (FC, AD), pp. 88–95.

ICML-2008-CortesMPR #algorithm

Stability of transductive regression algorithms (CC, MM, DP, AR), pp. 176–183.

ICML-2008-Rosset #kernel

Bi-level path following for cross validated solution of kernel quantile regression (SR), pp. 840–847.

ICML-2008-WalderKS #multi #process

Sparse multiscale gaussian process regression (CW, KIK, BS), pp. 1112–1119.

ICPR-2008-DingB #adaptation #analysis

Adaptive Laplacian eigenfunctions as bases for regression analysis (LD, XB), pp. 1–4.

ICPR-2008-FuR #learning #multi #performance

Fast multiple instance learning via L1, 2 logistic regression (ZF, ARK), pp. 1–4.

ICPR-2008-GuoFDH #classification #estimation #question

Head pose estimation: Classification or regression? (GG, YF, CRD, TSH), pp. 1–4.

ICPR-2008-KashimaYIS

Regression with interval output values (HK, KY, AI, HS), pp. 1–4.

ICPR-2008-NguyenLV #2d #locality

Ridge Regression for Two Dimensional Locality Preserving Projection (NTN, WL, SV), pp. 1–4.

ICPR-2008-PorroHTNDB #evaluation #performance

Performance evaluation of relevance vector machines as a nonlinear regression method in real-world chemical spectroscopic data (DP, NHG, ITB, ON, AD, RJB), pp. 1–4.

ICPR-2008-SuAL #predict #realtime #robust

Robust real-time face alignment based on ASM with boosting regression for displacement prediction (YS, HA, SL), pp. 1–4.

ICPR-2008-TranTJ #anti #linear #network #probability

An adjustable combination of linear regression and modified probabilistic neural network for anti-spam filtering (TPT, PT, TJ), pp. 1–4.

ICPR-2008-WatanabeK #locality #multi

Locality preserving multi-nominal logistic regression (KW, TK), pp. 1–4.

KDD-2008-ChangYM

Partitioned logistic regression for spam filtering (MWC, WtY, CM), pp. 97–105.

KDD-2008-IfrimBW #categorisation #n-gram #performance

Fast logistic regression for text categorization with variable-length n-grams (GI, GHB, GW), pp. 354–362.

KDD-2008-JinAXR #effectiveness #performance #summary

Effective and efficient itemset pattern summarization: regression-based approaches (RJ, MAA, YX, NR), pp. 399–407.

KDD-2008-SaigoKT #graph #mining

Partial least squares regression for graph mining (HS, NK, KT), pp. 578–586.

KDD-2008-SongJRG #linear

A bayesian mixture model with linear regression mixing proportions (XS, CJ, SR, JG), pp. 659–667.

KDD-2008-YuFRKRDL #analysis #privacy

Privacy-preserving cox regression for survival analysis (SY, GF, RR, SK, RBR, CDO, PL), pp. 1034–1042.

SEKE-2008-HernandezKPC #metamodelling #testing #web

A Meta-model to Support Regression Testing of Web Applications (YH, TMK, JP, PJC), pp. 500–505.

SIGIR-2008-XuA #feedback

A bayesian logistic regression model for active relevance feedback (ZX, RA), pp. 227–234.

SAC-2008-AnagnostopoulosAH #adaptation #data type #multi #what

Deciding what to observe next: adaptive variable selection for regression in multivariate data streams (CA, NMA, DJH), pp. 961–965.

SAC-2008-BragaOM #estimation #feature model #optimisation #parametricity

A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation (PLB, ALIO, SRLM), pp. 1788–1792.

FSE-2008-DoMTR #constraints #empirical #testing

An empirical study of the effect of time constraints on the cost-benefits of regression testing (HD, SM, LT, GR), pp. 71–82.

ICST-2008-HarmanA #automation #program repair #testing #web

Automated Session Data Repair for Web Application Regression Testing (MH, NA), pp. 298–307.

ISSTA-2008-DoR #analysis #modelling #testing #using

Using sensitivity analysis to create simplified economic models for regression testing (HD, GR), pp. 51–62.

ISSTA-2008-QuCR #empirical #testing

Configuration-aware regression testing: an empirical study of sampling and prioritization (XQ, MBC, GR), pp. 75–86.

ASE-2007-NaslavskyR #modelling #testing #traceability #using

Using traceability to support model-based regression testing (LN, DJR), pp. 567–570.

ASE-2007-ZhengWR #automation #named #testing

Pallino: automation to support regression test selection for cots-based applications (JZ, LW, BR), pp. 224–233.

DAC-2007-HallschmidS #automation #energy #modelling #using

Automatic Cache Tuning for Energy-Efficiency using Local Regression Modeling (PH, RS), pp. 732–737.

DAC-2007-SingheeR #performance #statistics

Beyond Low-Order Statistical Response Surfaces: Latent Variable Regression for Efficient, Highly Nonlinear Fitting (AS, RAR), pp. 256–261.

ICSM-2007-CapiluppiF #anti #open source #predict

A model to predict anti-regressive effort in Open Source Software (AC, JFR), pp. 194–203.

ICSM-2007-ChittimalliH #testing

Re-computing Coverage Information to Assist Regression Testing (PKC, MJH), pp. 164–173.

ICSM-2007-GallagherHB #testing

Reducing Regression Test Size by Exclusion (KG, TH, SB), pp. 154–163.

ICSM-2007-HouZXMS #component #interface #testing

Applying Interface-Contract Mutation in Regression Testing of Component-Based Software (SSH, LZ, TX, HM, JS), pp. 174–183.

ICSM-2007-QuCW #case study #combinator #generative #interactive #testing

Combinatorial Interaction Regression Testing: A Study of Test Case Generation and Prioritization (XQ, MBC, KMW), pp. 255–264.

CIKM-2007-CaiHZH #locality

Regularized locality preserving indexing via spectral regression (DC, XH, WVZ, JH), pp. 741–750.

ECIR-2007-PaltoglouSS #algorithm #modelling #multi #using

Results Merging Algorithm Using Multiple Regression Models (GP, MS, MS), pp. 173–184.

ICML-2007-BabariaNKSBM #crawling #scalability

Focused crawling with scalable ordinal regression solvers (RB, JSN, SK, KRS, CB, MNM), pp. 57–64.

ICML-2007-KerstingPPB #process

Most likely heteroscedastic Gaussian process regression (KK, CP, PP, WB), pp. 393–400.

ICML-2007-LinWK #scalability #trust

Trust region Newton methods for large-scale logistic regression (CJL, RCW, SSK), pp. 561–568.

ICML-2007-NilssonSJ #kernel #reduction #using

Regression on manifolds using kernel dimension reduction (JN, FS, MIJ), pp. 697–704.

ICML-2007-PetersS #learning

Reinforcement learning by reward-weighted regression for operational space control (JP, SS), pp. 745–750.

ICML-2007-WangYHLT

Transductive regression piloted by inter-manifold relations (HW, SY, TSH, JL, XT), pp. 967–974.

KDD-2007-VogelAS #linear #scalability

Scalable look-ahead linear regression trees (DSV, OA, TS), pp. 757–764.

SIGIR-2007-XuWL

Estimating collection size with logistic regression (JX, SW, XL), pp. 789–790.

SIGIR-2007-ZhengCSZ #framework #learning #ranking #using

A regression framework for learning ranking functions using relative relevance judgments (ZZ, KC, GS, HZ), pp. 287–294.

SAC-2007-MaoLZ #component #design #testing

Regression testing for component-based software via built-in test design (CM, YL, JZ), pp. 1416–1421.

ICSE-2007-MarianiPP #component #testing

Compatibility and Regression Testing of COTS-Component-Based Software (LM, SP, MP), pp. 85–95.

ICSE-2007-XuR #aspectj #testing

Regression Test Selection for AspectJ Software (G(X, AR), pp. 65–74.

HPCA-2007-LeeB #architecture #design #modelling

Illustrative Design Space Studies with Microarchitectural Regression Models (BCL, DMB), pp. 340–351.

AMOST-2007-ChenPU #analysis #dependence #generative #modelling #testing #using

Model-based regression test suite generation using dependence analysis (YC, RLP, HU), pp. 54–62.

AMOST-2007-FarooqIMN #approach #state machine #testing

An approach for selective state machine based regression testing (QuaF, MZZI, ZIM, AN), pp. 44–52.

MBT-2007-FraserAW #model checking #testing

Handling Model Changes: Regression Testing and Test-Suite Update with Model-Checkers (GF, BKA, FW), pp. 33–46.

CASE-2006-ShaoHM #algorithm #analysis #component #linear #multi #using

Quantitative Analysis Using NIR by Building Principal Component- Multiple Linear Regression-BP Algorithm (YS, YH, JM), pp. 161–164.

ICSM-2006-PilskalnsUA #design #testing #uml

Regression Testing UML Designs (OP, GU, AAA), pp. 254–264.

ICSM-2006-Sneed #migration #testing

Selective Regression Testing of a Host to DotNet Migration (HMS), pp. 104–112.

ICML-2006-BrefeldGSW #performance

Efficient co-regularised least squares regression (UB, TG, TS, SW), pp. 137–144.

ICML-2006-Garcke

Regression with the optimised combination technique (JG), pp. 321–328.

ICML-2006-TingDS

Bayesian regression with input noise for high dimensional data (JAT, AD, SS), pp. 937–944.

ICML-2006-WangYL #2d

Two-dimensional solution path for support vector regression (GW, DYY, FHL), pp. 993–1000.

ICML-2006-YuYTK #collaboration

Collaborative ordinal regression (SY, KY, VT, HPK), pp. 1089–1096.

ICPR-v1-2006-ZhuJB #estimation

Nonlinear Eye Gaze Mapping Function Estimation via Support Vector Regression (ZZ, QJ, KPB), pp. 1132–1135.

ICPR-v2-2006-DornaikaD #modelling #recognition #using

Facial Expression Recognition using Auto-regressive Models (FD, FD), pp. 520–523.

ICPR-v3-2006-GinnekenM #image #nearest neighbour

Image Denoising with k-nearest Neighbor and Support Vector Regression (BvG, AM), pp. 603–606.

ICPR-v3-2006-MaKKLK #estimation

Sparse Bayesian Regression for Head Pose Estimation (YM, YK, KK, SL, MK), pp. 507–510.

ICPR-v3-2006-WuT06a #image #re-engineering

A Regression Model in TensorPCA Subspace for Face Image Super-resolution Reconstruction (JW, MMT), pp. 627–630.

ICPR-v3-2006-YangZ #nearest neighbour #recognition

Regression Nearest Neighbor in Face Recognition (SY, CZ), pp. 515–518.

KDD-2006-FanMY #framework #performance #random #summary

A general framework for accurate and fast regression by data summarization in random decision trees (WF, JM, PSY), pp. 136–146.

KDD-2006-Jaroszewicz #polynomial

Polynomial association rules with applications to logistic regression (SJ), pp. 586–591.

KDD-2006-MeruguRP #approach #estimation #multi

A new multi-view regression approach with an application to customer wallet estimation (SM, SR, CP), pp. 656–661.

ECOOP-2006-Xie #automation #testing

Augmenting Automatically Generated Unit-Test Suites with Regression Oracle Checking (TX), pp. 380–403.

OOPSLA-2006-CavazosO #compilation #using

Method-specific dynamic compilation using logistic regression (JC, MFPO), pp. 229–240.

FSE-2006-DoR #empirical #modelling #testing

An empirical study of regression testing techniques incorporating context and lifetime factors and improved cost-benefit models (HD, GR), pp. 141–151.

FSE-2006-GirouxR #detection #testing #using

Detecting increases in feature coupling using regression tests (OG, MPR), pp. 163–174.

ICSE-2006-ZhengRWS #testing

Applying regression test selection for COTS-based applications (JZ, BR, LW, KS), pp. 512–522.

ASPLOS-2006-LeeB #architecture #modelling #performance #predict

Accurate and efficient regression modeling for microarchitectural performance and power prediction (BCL, DMB), pp. 185–194.

HPCA-2006-JosephVT #analysis #linear #modelling #performance

Construction and use of linear regression models for processor performance analysis (PJJ, KV, MJT), pp. 99–108.

ASE-2005-Zheng #source code #testing

In regression testing selection when source code is not available (JZ0), pp. 452–455.

ICSM-2005-HayesZ #analysis #evolution #maintenance #metric #predict

Maintainability Prediction: A Regression Analysis of Measures of Evolving Systems (JHH, LZ), pp. 601–604.

ICSM-2005-WhiteJR #object-oriented #testing

Utilization of Extended Firewall for Object-Oriented Regression Testing (LJW, KJ, BR), pp. 695–698.

ICSM-2005-WillmorE #testing

A Safe Regression Test Selection Technique for Database-Driven Applications (DW, SME), pp. 421–430.

ICML-2005-ChuK

New approaches to support vector ordinal regression (WC, SSK), pp. 145–152.

ICML-2005-CortesMW #learning

A general regression technique for learning transductions (CC, MM, JW), pp. 153–160.

ICML-2005-DriessensD #first-order #learning #modelling

Combining model-based and instance-based learning for first order regression (KD, SD), pp. 193–200.

ICML-2005-LeSC #process

Heteroscedastic Gaussian process regression (QVL, AJS, SC), pp. 489–496.

ICML-2005-LiaoXC #data flow

Logistic regression with an auxiliary data source (XL, YX, LC), pp. 505–512.

ICML-2005-TsangKL #problem #scalability

Core Vector Regression for very large regression problems (IWT, JTK, KTL), pp. 912–919.

ICML-2005-WilliamsLXC #classification #using

Incomplete-data classification using logistic regression (DW, XL, YX, LC), pp. 972–979.

KDD-2005-Torgo #fault

Regression error characteristic surfaces (LT), pp. 697–702.

SAC-2005-RummelKT #data flow #testing #towards

Towards the prioritization of regression test suites with data flow information (MJR, GMK, AT), pp. 1499–1504.

ASE-2004-PerssonY #automation #case study #experience #industrial #testing

Establishment of Automated Regression Testing at ABB: Industrial Experience Report on “Avoiding the Pitfalls” (CP, NY), pp. 112–121.

DAC-2004-FineUZ #functional #probability #verification

Probabilistic regression suites for functional verification (SF, SU, AZ), pp. 49–54.

DATE-v1-2004-Wang #learning #simulation #validation

Regression Simulation: Applying Path-Based Learning In Delay Test and Post-Silicon Validation (LCW), pp. 692–695.

CSMR-2004-Sneed #reverse engineering #testing

Reverse Engineering of Test Cases for Selective Regression Testing (HMS), pp. 69–74.

ICSM-2004-MemonX #detection #effectiveness #empirical #evaluation #testing

Empirical Evaluation of the Fault-Detection Effectiveness of Smoke Regression Test Cases for GUI-Based Software (AMM, QX), pp. 8–17.

ICSM-2004-SkoglundR #case study #evolution #maintenance #testing

A Case Study on Regression Test Suite Maintenance in System Evolution (MS, PR), pp. 438–442.

ICSM-2004-WhiteR #analysis #industrial #realtime #testing #using

Industrial Real-Time Regression Testing and Analysis Using Firewalls (LJW, BR), pp. 18–27.

ICSM-2004-XieN #black box #difference #testing

Checking Inside the Black Box: Regression Testing Based on Value Spectra Differences (TX, DN), pp. 28–37.

ICEIS-v3-2004-MukkamalaSAR #adaptation #detection #using

Intrusion Detection Systems Using Adaptive Regression Splines (SM, AHS, AA, VR), pp. 26–33.

ICML-2004-ZhangKY #algorithm

Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model (ZZ, JTK, DYY).

ICML-2004-ZhangY #estimation #probability

Probabilistic score estimation with piecewise logistic regression (JZ, YY).

ICPR-v1-2004-MiasnikovH #analysis #rank

Regression Analysis and Automorphic Orbits in Free Groups of Rank 2 (ADM, RMH), pp. 180–183.

ICPR-v2-2004-CawleyT #kernel #performance

Efficient Model Selection for Kernel Logistic Regression (GCC, NLCT), pp. 439–442.

ICPR-v3-2004-AdeodatoVASCM #case study #comparative #network #scalability #set

Neural Networks vs Logistic Regression: a Comparative Study on a Large Data Set (PJLA, GCV, ALA, RAFS, RCLVC, DSMPM), pp. 355–358.

ICPR-v3-2004-GinnekenL #image #segmentation

Pixel Position Regression — Application to Medical Image Segmentation (BvG, ML), pp. 718–721.

ICPR-v4-2004-DebiollesOA #network

Combined Use of Partial Least Squares Regression and Neural Network for Diagnosis Tasks (AD, LO, PA), pp. 573–576.

KDD-2004-AndersonMCN #performance

Fast nonlinear regression via eigenimages applied to galactic morphology (BA, AWM, AJC, RN), pp. 40–48.

KDD-2004-LazarevicKK #detection #effectiveness #locality #scalability

Effective localized regression for damage detection in large complex mechanical structures (AL, RK, CK), pp. 450–459.

KDD-2004-SanilKLR #distributed #modelling #privacy

Privacy preserving regression modelling via distributed computation (APS, AFK, XL, JPR), pp. 677–682.

SEKE-2004-CanforaCT #estimation #experience #fuzzy #linear

An Experience of Fuzzy Linear Regression applied to Effort Estimation (GC, LC, LT), pp. 57–61.

FSE-2004-OrsoSH #scalability #testing

Scaling regression testing to large software systems (AO, NS, MJH), pp. 241–251.

ICDAR-2003-ZhangT #documentation #image

Correcting Document Image Warping Based on Regression of Curved Text Lines (ZZ, CLT), pp. 589–593.

VLDB-2003-TengCY #data type #mining

A Regression-Based Temporal Pattern Mining Scheme for Data Streams (WGT, MSC, PSY), pp. 93–104.

ICSM-2003-KojuTD #testing #virtual machine

Regression Test Selection based on Intermediate Code for Virtual Machines (TK, ST, ND), p. 420–?.

ICSM-2003-MemonBHN #framework #named #testing #user interface

DART: A Framework for Regression Testing “Nightly/daily Builds” of GUI Applications (AMM, IB, NH, AN), pp. 410–419.

ICSM-2003-WhiteAS #interactive #sequence #testing #user interface

Firewall Regression Testing of GUI Sequences and their Interactions (LJW, HA, SS), pp. 398–409.

CIKM-2003-YangK #adaptation

Margin-based local regression for adaptive filtering (YY, BK), pp. 191–198.

ICML-2003-BiB #fault

Regression Error Characteristic Curves (JB, KPB), pp. 43–50.

ICML-2003-DriessensR #learning #relational

Relational Instance Based Regression for Relational Reinforcement Learning (KD, JR), pp. 123–130.

ICML-2003-LeeL #learning #using

Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (WSL, BL), pp. 448–455.

ICML-2003-ZhangJYH #approximate #categorisation #scalability

Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization (JZ, RJ, YY, AGH), pp. 888–895.

KDD-2003-YuC #distributed #multi

Distributed multivariate regression based on influential observations (HY, ECC), pp. 679–684.

MLDM-2003-CeciAM

Simplification Methods for Model Trees with Regression and Splitting Nodes (MC, AA, DM), pp. 20–34.

ESEC-FSE-2003-MemonS #testing #user interface

Regression testing of GUIs (AMM, MLS), pp. 118–127.

ESEC-FSE-2003-OrsoAH #impact analysis #testing

Leveraging field data for impact analysis and regression testing (AO, TA, MJH), pp. 128–137.

TestCom-2003-MittalA #framework #testing

An Open Framework for Managed Regression Testing (NM, IA), pp. 265–278.

VLDB-2002-ChenHWW #analysis #data type #multi

Multi-Dimensional Regression Analysis of Time-Series Data Streams (YC, GD, JH, BWW, JW), pp. 323–334.

ICSM-2002-BriandLS #automation #design #impact analysis #testing #uml

Automating Impact Analysis and Regression Test Selection Based on UML Designs (LCB, YL, GS), pp. 252–261.

ICSM-2002-KorelTV #analysis #dependence #modelling #reduction #testing #using

Model Based Regression Test Reduction Using Dependence Analysis (BK, LHT, BV), pp. 214–223.

ICSM-2002-MalishevskyRE #modelling #testing #trade-off

Modeling the Cost-Benefits Tradeoffs for Regression Testing Techniques (AGM, GR, SGE), pp. 204–213.

ICML-2002-BringmannKNPW

Transformation-Based Regression (BB, SK, FN, HP, GW), pp. 59–66.

ICML-2002-KeerthiDSP #algorithm #kernel #performance

A Fast Dual Algorithm for Kernel Logistic Regression (SSK, KD, SKS, ANP), pp. 299–306.

ICML-2002-ThamDR #classification #learning #markov #monte carlo #using

Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo (SST, AD, KR), pp. 634–641.

KDD-2002-DobraG #algorithm #linear #named #scalability

SECRET: a scalable linear regression tree algorithm (AD, JG), pp. 481–487.

SIGIR-2002-Larson #approach #distributed #information retrieval

A logistic regression approach to distributed IR (RRL), pp. 399–400.

SIGIR-2002-SiC #using

Using sampled data and regression to merge search engine results (LS, JPC), pp. 19–26.

SAC-2002-DolinBR #co-evolution #distributed #effectiveness

Co-evolving an effective fitness sample: experiments in symbolic regression and distributed robot control (BD, FHBI, EGR), pp. 553–559.

ICSE-2002-KimP #testing

A history-based test prioritization technique for regression testing in resource constrained environments (JMK, AAP), pp. 119–129.

ICSE-2002-RothermelEMKD #effectiveness #testing

The impact of test suite granularity on the cost-effectiveness of regression testing (GR, SGE, AGM, PK, BD), pp. 130–140.

CBSE-2001-Weide #component #composition #testing

Modular Regression Testing: Connections to Component-Based Software (BWW), p. 11.

ICDAR-2001-WengH #classification #image #incremental #online

Incremental Hierarchical Discriminant Regression for Online Image Classification (JW, WSH), pp. 476–480.

ICSM-2001-OrsoHRRSD #component #testing #using

Using Component Metacontent to Support the Regression Testing of Component-Based Software (AO, MJH, DSR, GR, MLS, HD), pp. 716–725.

ICML-2001-ChuKO #framework

A Unified Loss Function in Bayesian Framework for Support Vector Regression (WC, SSK, CJO), pp. 51–58.

ICML-2001-NairCK #algorithm

Some Greedy Algorithms for Sparse Nonlinear Regression (PBN, AC, AJK), pp. 369–376.

ICML-2001-NouretdinovMV

Ridge Regression Confidence Machine (IN, TM, VV), pp. 385–392.

ICML-2001-RayP #multi

Multiple Instance Regression (SR, DP), pp. 425–432.

ICML-2001-Zhang #approximate #bound #problem

Some Sparse Approximation Bounds for Regression Problems (TZ0), pp. 624–631.

KDD-2001-BujaL #classification #data mining #mining

Data mining criteria for tree-based regression and classification (AB, YSL), pp. 27–36.

KDD-2001-IndurkhyaW #classification #problem #rule-based

Solving regression problems with rule-based ensemble classifiers (NI, SMW), pp. 287–292.

MLDM-2001-IndurkhyaW #rule-based

Rule-Based Ensemble Solutions for Regression (NI, SMW), pp. 62–72.

SEKE-2001-JorgensenIS #estimation #towards

Software effort estimation by analogy and regression toward the mean (MJ, UI, DIKS), pp. 268–274.

OOPSLA-2001-HarroldJLLOPSSG #java #testing

Regression Test Selection for Java Software (MJH, JAJ, TL, DL, AO, MP, SS, SAS, AG), pp. 312–326.

SAC-2001-HaratyMD #database #testing

Regression testing of database applications (RAH, NM, BD), pp. 285–289.

ICML-2000-SridharanT #automation #multi

Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions (MS, GT), pp. 927–934.

ICML-2000-VijayakumarS #incremental #learning #realtime

Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space (SV, SS), pp. 1079–1086.

ICPR-v2-2000-MenardDC #ambiguity #analysis #distance #modelling #using

Switching Regression Models Using Ambiguity and Distance Rejects: Application to Ionogram Analysis (MM, PAD, VC), pp. 2688–2691.

ICPR-v3-2000-LuceySC #linear #performance #using

Initialized Eigenlip Estimator for Fast Lip Tracking Using Linear Regression (SL, SS, VC), pp. 3182–3185.

ICSE-2000-KimPR #empirical #testing

An empirical study of regression test application frequency (JMK, AAP, GR), pp. 126–135.

ISSTA-2000-ElbaumMR #testing

Prioritizing test cases for regression testing (SGE, AGM, GR), pp. 102–112.

CSMR-1999-GranjaJ #testing

Techniques for Regression Testing: Selecting Test Case Sets Tailored to Possibly Modified Functionalities (IG, MJ), pp. 2–13.

KDD-1999-GaffneyS #clustering #modelling

Trajectory Clustering with Mixtures of Regression Models (SG, PS), pp. 63–72.

KDD-T-1999-GehrkeLR #classification

Classification and Regression: Money *can* Grow on Trees (JG, WYL, RR), pp. 1–73.

ICSM-1998-VokolosF #difference #empirical #evaluation #testing

Empirical Evaluation of the Textual Differencing Regression Testing Technique (FIV, PGF), pp. 44–53.

CIKM-1998-Chen #comparison #information retrieval #pattern matching #pattern recognition #recognition

A Comparison of Regression, Neural Net, and Pattern Recognition Approaches to IR (AC), pp. 140–147.

ICML-1998-SaundersGV #algorithm #learning

Ridge Regression Learning Algorithm in Dual Variables (CS, AG, VV), pp. 515–521.

ICPR-1998-Kwok #classification #problem

Support vector mixture for classification and regression problems (JTYK), pp. 255–258.

ICSE-1998-GravesHKPR #empirical #testing

An Empirical Study of Regression Test Selection Techniques (TLG, MJH, JMK, AAP, GR), pp. 188–197.

ISSTA-1998-Ball #analysis #control flow #on the #testing

On the Limit of Control Flow Analysis for Regression Test Selection (TB), pp. 134–142.

ISSTA-1998-KorelA #automation #generative #testing

Automated Regression Test Generation (BK, AMAY), pp. 143–152.

VLDB-1997-MorimotoIM #performance

Efficient Construction of Regression Trees with Range and Region Splitting (YM, HI, SM), pp. 166–175.

ICML-1997-MooreSD #performance #polynomial #predict

Efficient Locally Weighted Polynomial Regression Predictions (AWM, JGS, KD), pp. 236–244.

ICML-1997-Robnik-SikonjaK #adaptation #estimation

An adaptation of Relief for attribute estimation in regression (MRS, IK), pp. 296–304.

ICML-1997-Torgo #functional #modelling

Functional Models for Regression Tree Leaves (LT), pp. 385–393.

KDD-1997-Drucker #classification #performance

Fast Committee Machines for Regression and Classification (HD), pp. 159–162.

ICSM-1996-White #interactive #testing #user interface

Regression Testing of GUI Event Interactions (LJW), pp. 350–358.

FSE-1996-RosenblumW #effectiveness #predict #testing

Predicting the Cost-Effectiveness of Regression Testing Strategies (DSR, EJW), pp. 118–126.

ICSM-1995-AbdullahKW #integration #testing

Correcting for unreliable regression integration testing (KA, JEKJ, LJW), pp. 232–241.

ICSM-1995-Binkley #cost analysis #semantics #testing

Reducing the cost of regression testing by semantics guided test case selection (DB), p. 251–?.

SAC-1995-CrawfordVW #algorithm #detection #multi #search-based #using

Detecting multiple outliers in regression data using genetic algorithms (KDC, DJV, RLW), pp. 351–356.

ICSM-1994-MayrhauserMW #testing

Domain Based Regression Testing (AvM, RTM, JW), pp. 26–35.

ICSM-1994-RothermelH #object-oriented #testing

Selecting Regression Tests for Object-Oriented Software (GR, MJH), pp. 14–25.

SIGIR-1994-Gey #probability #using

Inferring Probability of Relevance Using the Method of Logistic Regression (FCG), pp. 222–231.

ICSE-1994-ChenRV #named #testing

TestTube: A System for Selective Regression Testing (YFC, DSR, KPV), pp. 211–220.

ICSE-1994-RothermelH #framework #testing

A Framework for Evaluating Regression Test Selection Techniques (GR, MJH), pp. 201–210.

CSM-1993-AgrawalHKL #incremental #testing

Incremental Regression Testing (HA, JRH, EWK, SL), pp. 348–357.

CSM-1993-RothermelH #algorithm #performance #testing

A Safe, Efficient Algorithm for Regression Test Selection (GR, MJH), pp. 358–367.

CSM-1993-WhiteNFKPO #testing

Test Manager: A Regression Testing Tool (LJW, VN, TF, MK, PP, MO), pp. 338–347.

TRI-Ada-1993-MayrhauserJ #architecture #automation #knowledge-based #testing

CASE Tool Architecture for Knowledge-Based Regression Testing (AvM, TJ), pp. 368–378.

ESEC-1993-LiuRE #database #testing

A Regression Testing Database Model (LL, DJR, RE), pp. 163–174.

ICSE-1993-LaskiSL #analysis #mutation testing #testing

Dynamic Mutation Testing in Integrated Regression Analysis (JWL, WS, PL), pp. 108–117.

ML-1992-Moulet #algorithm

A Symbolic Algorithm for Computing Coefficients’ Accuracy in Regression (MM), pp. 332–337.

SIGIR-1992-CooperGD #probability #retrieval #staged

Probabilistic Retrieval Based on Staged Logistic Regression (WSC, FCG, DPD), pp. 198–210.

ESEC-1989-LewisBH #named #testing

Assay — A Tool to Support Regression Testing (RL, DWB, JH), pp. 487–496.

ICSE-1985-MillerS

Completely Monotone Regression Estimates of Software Failure Rates (DRM, AS), pp. 343–349.

SIGMOD-1984-Fedorowicz #database #evaluation #multi #using

Database Evaluation Using Multiple Regression Techniques (JF), pp. 70–76.

STOC-1972-WarkentinF

Predecessor Machines and Regressing Functions (JCW, PCF), pp. 81–87.