David Lo, Sven Apel, Sarfraz Khurshid
Proceedings of the 31st International Conference on Automated Software Engineering
ASE, 2016.

@proceedings{ASE-2016,
	doi           = "10.1145/2970276",
	editor        = "David Lo and Sven Apel and Sarfraz Khurshid",
	isbn          = "978-1-4503-3845-5",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 31st International Conference on Automated Software Engineering}",
	year          = 2016,
}

Contents (100 items)

ASE-2016-Puschel #generative #performance

Program generation for performance (MP), p. 1.

ASE-2016-Schulte #evolution

Changing microsoft’s build: revolution or evolution (WS), p. 2.

ASE-2016-Rosenblum #power of #probability

The power of probabilistic thinking (DSR), p. 3.

ASE-2016-TufanoPBPOLP #empirical #smell

An empirical investigation into the nature of test smells (MT, FP, GB, MDP, RO, ADL, DP), pp. 4–15.

ASE-2016-AlipourSGMG #reduction #testing

Evaluating non-adequate test-case reduction (MAA, AS, RG, DM, AG), pp. 16–26.

ASE-2016-OhmannBNLL #optimisation

Optimizing customized program coverage (PO, DBB, NN, JL, BL), pp. 27–38.

ASE-2016-Visser #what

What makes killing a mutant hard (WV), pp. 39–44.

ASE-2016-StratisR #execution #permutation #testing

Test case permutation to improve execution time (PS, AR), pp. 45–50.

ASE-2016-XuYXXCL #developer #network #online #predict #semantics

Predicting semantically linkable knowledge in developer online forums via convolutional neural network (BX, DY, ZX, XX, GC, SL), pp. 51–62.

ASE-2016-AbdessalemNBS #multi #network #testing #using

Testing advanced driver assistance systems using multi-objective search and neural networks (RBA, SN, LCB, TS), pp. 63–74.

ASE-2016-QiJZWC #estimation #learning #obfuscation #privacy #subclass

Privacy preserving via interval covering based subclass division and manifold learning based bi-directional obfuscation for effort estimation (FQ, XYJ, XZ, FW, LC), pp. 75–86.

ASE-2016-WhiteTVP #clone detection #detection #learning

Deep learning code fragments for code clone detection (MW, MT, CV, DP), pp. 87–98.

ASE-2016-HannebauerPSG #automation #code review #comparison #empirical #recommendation

Automatically recommending code reviewers based on their expertise: an empirical comparison (CH, MP, SS, VG), pp. 99–110.

ASE-2016-ProkschANM #recommendation

Evaluating the evaluations of code recommender systems: a reality check (SP, SA, SN, MM), pp. 111–121.

ASE-2016-KrishnaMF #automation #learning

Too much automation? the bellwether effect and its implications for transfer learning (RK, TM, WF), pp. 122–131.

ASE-2016-Rodriguez-Cancio #automation #benchmark #constant #generative

Automatic microbenchmark generation to prevent dead code elimination and constant folding (MRC, BC, BB), pp. 132–143.

ASE-2016-Tzoref-BrillWM #combinator #modelling #testing #visualisation

Visualization of combinatorial models and test plans (RTB, PW, SM), pp. 144–154.

ASE-2016-BocicB #data access #debugging #web

Finding access control bugs in web applications with CanCheck (IB, TB), pp. 155–166.

ASE-2016-CeccatoNAB #automation #black box #injection #named #security #testing

SOFIA: an automated security oracle for black-box testing of SQL-injection vulnerabilities (MC, CDN, DA, LCB), pp. 167–177.

ASE-2016-ChenBHZ0XM #static analysis

Supporting oracle construction via static analysis (JC, YB, DH, LZ, LZ, BX, HM), pp. 178–189.

ASE-2016-WangWCW #classification #crowdsourcing #testing

Local-based active classification of test report to assist crowdsourced testing (JW, SW, QC, QW), pp. 190–201.

ASE-2016-FengJCF #comprehension #image #multi #using

Multi-objective test report prioritization using image understanding (YF, JAJ, ZC, CF), pp. 202–213.

ASE-2016-PengGT0YNZ #composition #crowdsourcing #mobile #named

CrowdService: serving the individuals through mobile crowdsourcing and service composition (XP, JG, THT, JS, YY, BN, WZ), pp. 214–219.

ASE-2016-RahmanR #automation #concept #crowdsourcing #named #query #using

QUICKAR: automatic query reformulation for concept location using crowdsourced knowledge (MMR, CKR), pp. 220–225.

ASE-2016-WeiLC #android #detection

Taming Android fragmentation: characterizing and detecting compatibility issues for Android apps (LW, YL, SCC), pp. 226–237.

ASE-2016-BaekB #android #automation #comparison #modelling #multi #testing #user interface #using

Automated model-based Android GUI testing using multi-level GUI comparison criteria (YMB, DHB), pp. 238–249.

ASE-2016-LeeDR #android #framework #hybrid #named #static analysis

HybriDroid: static analysis framework for Android hybrid applications (SL, JD, SR), pp. 250–261.

ASE-2016-WenWC #debugging #named

Locus: locating bugs from software changes (MW, RW, SCC), pp. 262–273.

ASE-2016-LaghariMD #fault #locality #set

Fine-tuning spectrum based fault localisation with frequent method item sets (GL, AM, SD), pp. 274–285.

ASE-2016-AlmhanaMK0 #debugging #multi #recommendation #using

Recommending relevant classes for bug reports using multi-objective search (RA, WM, MK, AO), pp. 286–295.

ASE-2016-YangHKIBZX #clustering #dependence #empirical #predict

An empirical study on dependence clusters for effort-aware fault-proneness prediction (YY, MH, JK, SSI, DB, YZ, BX), pp. 296–307.

ASE-2016-MingWWXL #analysis #named

StraightTaint: decoupled offline symbolic taint analysis (JM, DW, JW, GX, PL), pp. 308–319.

ASE-2016-SzaboEV #analysis #domain-specific language #incremental #named

IncA: a DSL for the definition of incremental program analyses (TS, SE, MV), pp. 320–331.

ASE-2016-ChristakisB #developer #empirical #program analysis #what

What developers want and need from program analysis: an empirical study (MC, CB), pp. 332–343.

ASE-2016-CaiT #distributed #effectiveness #impact analysis #named #source code

DistIA: a cost-effective dynamic impact analysis for distributed programs (HC, DT), pp. 344–355.

ASE-2016-CaiY #probability #testing

Radius aware probabilistic testing of deadlocks with guarantees (YC, ZY), pp. 356–367.

ASE-2016-LinZCZ #api #detection #java #named

LockPeeker: detecting latent locks in Java APIs (ZL, HZ, YC, JZ), pp. 368–378.

ASE-2016-KroeningPSW #analysis #concurrent #thread

Sound static deadlock analysis for C/Pthreads (DK, DP, PS, BW), pp. 379–390.

ASE-2016-VojdaniARSVV #approach #concurrent #detection

Static race detection for device drivers: the Goblint approach (VV, KA, VR, HS, VV, RV), pp. 391–402.

ASE-2016-HentschelHB #empirical #evaluation #interactive #user interface #verification

An empirical evaluation of two user interfaces of an interactive program verifier (MH, RH, RB), pp. 403–413.

ASE-2016-MaroAWS #guidelines #maintenance #traceability

Traceability maintenance: factors and guidelines (SM, AA, RW, JPS), pp. 414–425.

ASE-2016-HiltonTHMD #cost analysis #integration #open source

Usage, costs, and benefits of continuous integration in open-source projects (MH, TT, KH, DM, DD), pp. 426–437.

ASE-2016-PescadorL #design #domain-specific language #named #requirements

DSL-maps: from requirements to design of domain-specific languages (AP, JdL), pp. 438–443.

ASE-2016-Asenov0V #ide #information management

The IDE as a scriptable information system (DA, PM, LV), pp. 444–449.

ASE-2016-PavlinovicLS #verification

Inferring annotations for device drivers from verification histories (ZP, AL, RS), pp. 450–460.

ASE-2016-MaasNL #array #c #library

Array length inference for C library bindings (AJM, HN, BL), pp. 461–471.

ASE-2016-KangRJ #api #automation #c #fault #named #specification

APEx: automated inference of error specifications for C APIs (YJK, BR, SJ), pp. 472–482.

ASE-2016-MeinickeWKTS #complexity #configuration management #interactive #on the

On essential configuration complexity: measuring interactions in highly-configurable systems (JM, CPW, CK, TT, GS), pp. 483–494.

ASE-2016-LiZRC #precise #refinement #semantics #slicing

Precise semantic history slicing through dynamic delta refinement (YL, CZ, JR, MC), pp. 495–506.

ASE-2016-DegiovanniRACA #detection #satisfiability

Goal-conflict detection based on temporal satisfiability checking (RD, NR, DA, PFC, NA), pp. 507–518.

ASE-2016-MahmoodGS #database #execution #symbolic computation

Symbolic execution of stored procedures in database management systems (MSM, MAG, JHS), pp. 519–530.

ASE-2016-GuoKW #concurrent #execution #incremental #named #symbolic computation

Conc-iSE: incremental symbolic execution of concurrent software (SG, MK, CW), pp. 531–542.

ASE-2016-PhamBR #fuzzing #modelling

Model-based whitebox fuzzing for program binaries (VTP, MB, AR), pp. 543–553.

ASE-2016-LiLQHBYCL #constraints #execution #machine learning #symbolic computation #theorem proving

Symbolic execution of complex program driven by machine learning based constraint solving (XL, YL, HQ, YQH, LB, YY, XC, XL), pp. 554–559.

ASE-2016-Nishi #bound #model checking #programming #towards #using

Towards bounded model checking using nonlinear programming solver (MN), pp. 560–565.

ASE-2016-ThakurG #identification #specification

Identifying domain elements from textual specifications (JST, AG), pp. 566–577.

ASE-2016-PeldszusKLS #design #detection #evolution #incremental #multi #object-oriented #pattern matching #source code #using

Continuous detection of design flaws in evolving object-oriented programs using incremental multi-pattern matching (SP, GK, ML, SS), pp. 578–589.

ASE-2016-DemuthRE #consistency #detection #developer #multi #nondeterminism #performance

Efficient detection of inconsistencies in a multi-developer engineering environment (AD, MRE, AE), pp. 590–601.

ASE-2016-LegunsenHXRM #api #case study #effectiveness #how #java #specification

How good are the specs? a study of the bug-finding effectiveness of existing Java API specifications (OL, WUH, XX, GR, DM), pp. 602–613.

ASE-2016-YamadaBAKC #combinator #generative #satisfiability #testing #using

Greedy combinatorial test case generation using unsatisfiable cores (AY, AB, CA, TK, EHC), pp. 614–624.

ASE-2016-ZhangHC #automation #generative #testing #towards

Towards automatically generating descriptive names for unit tests (BZ, EH, JC), pp. 625–636.

ASE-2016-LiLKLG #big data #combinator #generative #testing

Applying combinatorial test data generation to big data applications (NL, YL, HRK, JL, YG), pp. 637–647.

ASE-2016-TangWWZ #android #concurrent #debugging #generative #testing

Generating test cases to expose concurrency bugs in Android applications (HT, GW, JW, HZ), pp. 648–653.

ASE-2016-PatrickCSG #automation #generative #image #using

Automatic test image generation using procedural noise (MP, MDC, ROJHS, CAG), pp. 654–659.

ASE-2016-DotzlerP #difference #source code

Move-optimized source code tree differencing (GD, MP), pp. 660–671.

ASE-2016-MazinanianT #css #migration #mixin #preprocessor

Migrating cascading style sheets to preprocessors by introducing mixins (DM, NT), pp. 672–683.

ASE-2016-GuSMLS #automation #fault #runtime #synthesis

Automatic runtime recovery via error handler synthesis (TG, CS, XM, JL, ZS), pp. 684–695.

ASE-2016-ChengPJZYZ #detection #mining

Mining revision histories to detect cross-language clones without intermediates (XC, ZP, LJ, HZ, HY, JZ), pp. 696–701.

ASE-2016-CitoRSR #mobile

Battery-aware transformations in mobile applications (JC, JR, PSM, MR), pp. 702–707.

ASE-2016-WangCMT #debugging #detection #modelling #n-gram #named

Bugram: bug detection with n-gram language models (SW, DC, DMA, LT), pp. 708–719.

ASE-2016-HoscheleZ #mining

Mining input grammars from dynamic taints (MH, AZ), pp. 720–725.

ASE-2016-VuPNN #mobile

Phrase-based extraction of user opinions in mobile app reviews (PMV, HVP, TTN, TTN), pp. 726–731.

ASE-2016-MoonenABR #guidelines #mining #recommendation #using

Practical guidelines for change recommendation using association rule mining (LM, SDA, DB, TR), pp. 732–743.

ASE-2016-ChenCXX #learning #retrieval

Learning a dual-language vector space for domain-specific cross-lingual question retrieval (GC, CC, ZX, BX), pp. 744–755.

ASE-2016-LiBOK #android #static analysis

Reflection-aware static analysis of Android apps (LL, TFB, DO, JK), pp. 756–761.

ASE-2016-WuLDYZ #android #detection #effectiveness #named #static analysis

Relda2: an effective static analysis tool for resource leak detection in Android apps (TW, JL, XD, JY, JZ), pp. 762–767.

ASE-2016-ZhaiCNWFC #development #mobile

An end-user oriented tool suite for development of mobile applications (ZZ, BC, MN, ZW, YF, JC), pp. 768–773.

ASE-2016-ZhangJLZGS #design #embedded #modelling

Model driven design of heterogeneous synchronous embedded systems (HZ, YJ, HL, HZ, MG, JGS), pp. 774–779.

ASE-2016-OgnawalaOPL #analysis #composition #execution #low level #named #symbolic computation

MACKE: compositional analysis of low-level vulnerabilities with symbolic execution (SO, MO, AP, TL), pp. 780–785.

ASE-2016-GaoWL #automation #named

BovInspector: automatic inspection and repair of buffer overflow vulnerabilities (FG, LW, XL), pp. 786–791.

ASE-2016-RahmanRRC #code review #git #named #recommendation

CORRECT: code reviewer recommendation at GitHub for Vendasta technologies (MMR, CKR, JR, JAC), pp. 792–797.

ASE-2016-RazaF #analysis #automation #development #named #performance #recommendation

ProcessPAIR: a tool for automated performance analysis and improvement recommendation in software development (MR, JPF), pp. 798–803.

ASE-2016-Greene0 #developer #identification #mining #named #open source

CVExplorer: identifying candidate developers by mining and exploring their open source contributions (GJG, BF), pp. 804–809.

ASE-2016-KowarkMUP #lightweight #repository

Lightweight collection and storage of software repository data with DataRover (TK, CM, MU, HP), pp. 810–815.

ASE-2016-AlshanqitiHK #contract #dynamic analysis #reverse engineering #using #visual notation

Visual contract extractor: a tool for reverse engineering visual contracts using dynamic analysis (AMA, RH, TK), pp. 816–821.

ASE-2016-SchwagerlW #collaboration #modelling #named #product line #tool support

SuperMod: tool support for collaborative filtered model-driven software product line engineering (FS, BW), pp. 822–827.

ASE-2016-ThakurG16a #domain model #generative #modelling #named #specification

AnModeler: a tool for generating domain models from textual specifications (JST, AG), pp. 828–833.

ASE-2016-ChenX #automation #library #named #programming language #recommendation

SimilarTech: automatically recommend analogical libraries across different programming languages (CC, ZX), pp. 834–839.

ASE-2016-LiuGAA #automation #integration #named #testing #tool support

TeeVML: tool support for semi-automatic integration testing environment emulation (JL, JCG, IA, MAA), pp. 840–845.

ASE-2016-HentschelHB16a #comprehension #debugging #effectiveness #interactive #proving #verification

The interactive verification debugger: effective understanding of interactive proof attempts (MH, RH, RB), pp. 846–851.

ASE-2016-YangJGS #approach #automaton #verification

Verifying simulink stateflow model: timed automata approach (YY, YJ, MG, JGS), pp. 852–857.

ASE-2016-ChengCYW #as a service #named #testing #user interface

GUICat: GUI testing as a service (LC, JC, ZY, CW), pp. 858–863.

ASE-2016-MoketarKSRG #automation #collaboration #requirements #validation

An automated collaborative requirements engineering tool for better validation of requirements (NAM, MK, SS, MR, JCG), pp. 864–869.

ASE-2016-SzaboAVE #analysis #data flow #framework

An extensible framework for variable-precision data-flow analyses in MPS (TS, SA, MV, SE), pp. 870–875.

ASE-2016-Le #automation #effectiveness #performance #program repair #towards

Towards efficient and effective automatic program repair (XBDL), pp. 876–879.

ASE-2016-Keng #automation #behaviour #mobile #privacy #testing

Automated testing and notification of mobile app privacy leak-cause behaviours (JCJK), pp. 880–883.

ASE-2016-Mougouei #dependence #graph #integer #programming #requirements #using

Factoring requirement dependencies in software requirement selection using graphs and integer programming (DM), pp. 884–887.

ASE-2016-Babur #analysis #modelling #scalability #set #statistics

Statistical analysis of large sets of models (ÖB), pp. 888–891.

ASE-2016-Cito #developer #development #runtime

Developer targeted analytics: supporting software development decisions with runtime information (JC), pp. 892–895.

ASE-2016-Thung #api #development #recommendation

API recommendation system for software development (FT), pp. 896–899.