Proceedings of the 34th International Conference on Automated Software Engineering
ASE, 2019.

@proceedings{ASE-2019,
	ee            = "https://ieeexplore.ieee.org/xpl/conhome/8949433/proceeding",
	isbn          = "978-1-7281-2508-4",
	publisher     = "{IEEE}",
	title         = "{Proceedings of the 34th International Conference on Automated Software Engineering}",
	year          = 2019,
}

Contents (154 items)

ASE-2019-KangB0

Assessing the Generalizability of Code2vec Token Embeddings (HJK, TFB, DL0), pp. 1–12.

ASE-2019-WanSSXZ0Y #learning #multi #network #retrieval #semantics #source code

Multi-modal Attention Network Learning for Semantic Source Code Retrieval (YW, JS, YS, GX, ZZ, JW0, PSY), pp. 13–25.

ASE-2019-Gladisch0HOVP #automation #experience #search-based #testing

Experience Paper: Search-Based Testing in Automated Driving Control Applications (CG, TH0, CH, JO, AvV, TP), pp. 26–37.

ASE-2019-BuiYJ #named #network

AutoFocus: Interpreting Attention-Based Neural Networks by Code Perturbation (NDQB, YY, LJ), pp. 38–41.

ASE-2019-LinJM #mobile #semantics

Test Transfer Across Mobile Apps Through Semantic Mapping (JWL, RJ, SM), pp. 42–53.

ASE-2019-BehrangO #migration #mobile

Test Migration Between Mobile Apps with Similar Functionality (FB, AO), pp. 54–65.

ASE-2019-LiuW0B0X #android #detection #named

DaPanda: Detecting Aggressive Push Notifications in Android Apps (TL, HW, LL0, GB, YG0, GX), pp. 66–78.

ASE-2019-YangJ0WSLZX #automation #self #test coverage

Automatic Self-Validation for Code Coverage Profilers (YY, YJ0, ZZ0, YW, HS, HL, YZ, BX), pp. 79–90.

ASE-2019-GodioBPAF #generative #performance #test coverage #testing

Efficient Test Generation Guided by Field Coverage Criteria (AG, VSB, PP, NA, MFF), pp. 91–101.

ASE-2019-LuoBS #analysis #android

A Qualitative Analysis of Android Taint-Analysis Results (LL, EB, JS), pp. 102–114.

ASE-2019-LaiR #android

Goal-Driven Exploration for Android Applications (DL, JR), pp. 115–127.

ASE-2019-SahinAMCE #android #named #runtime

RANDR: Record and Replay for Android Applications via Targeted Runtime Instrumentation (OS, AA, HM, AKC, ME), pp. 128–138.

ASE-2019-WuLZYZ0 #analysis #mobile #named #performance

MalScan: Fast Market-Wide Mobile Malware Scanning by Social-Network Centrality Analysis (YW, XL, DZ, WY0, XZ, HJ0), pp. 139–150.

ASE-2019-RenX00S #community

Discovering, Explaining and Summarizing Controversial Discussions in Community Q&A Sites (XR, ZX, XX0, GL0, JS), pp. 151–162.

ASE-2019-GaoZX0LK #automation #generative #overview

Automating App Review Response Generation (CG, JZ, XX0, DL0, MRL, IK), pp. 163–175.

ASE-2019-Liu0T0L #automation #generative

Automatic Generation of Pull Request Descriptions (ZL, XX0, CT, DL0, SL), pp. 176–188.

ASE-2019-HavrikovZ

Systematically Covering Input Structure (NH, AZ), pp. 189–199.

ASE-2019-SondhiP #consistency #named #nondeterminism #semantics #specification #string

SEGATE: Unveiling Semantic Inconsistencies between Code and Specification of String Inputs (DS, RP), pp. 200–212.

ASE-2019-JiaLYLWLL #debugging #detection #fault #specification

Detecting Error-Handling Bugs without Error Specification Input (ZJ, SL, TY, XL, JW, XL, YL), pp. 213–225.

ASE-2019-Osei-OwusuAB0C #testing

Grading-Based Test Suite Augmentation (JOO, AA, LB, TX0, GC), pp. 226–229.

ASE-2019-Wang #empirical #evaluation

Emotions Extracted from Text vs. True Emotions-An Empirical Evaluation in SE Context (YW), pp. 230–242.

ASE-2019-SaifullahAR #api #learning

Learning from Examples to Find Fully Qualified Names of API Elements in Code Snippets (CMKS, MA, CKR), pp. 243–254.

ASE-2019-JiangRXZ #program transformation

Inferring Program Transformations From Singular Examples via Big Code (JJ, LR, YX, LZ), pp. 255–266.

ASE-2019-HeLWMZLHLX #algorithm #analysis

Performance-Boosting Sparsification of the IFDS Algorithm with Applications to Taint Analysis (DH, HL, LW, HM, HZ, JL0, SH, LL, JX), pp. 267–279.

ASE-2019-WangLXMG #android

Characterizing Android App Signing Issues (HW, HL, XX, GM, YG), pp. 280–292.

ASE-2019-RahatFT #android #debugging #empirical #named

OAUTHLINT: An Empirical Study on OAuth Bugs in Android Applications (TAR, YF, YT), pp. 293–304.

ASE-2019-ChenWHXZZ #compilation #generative

History-Guided Configuration Diversification for Compiler Test-Program Generation (JC, GW, DH, YX, HZ, LZ), pp. 305–316.

ASE-2019-StepanovAB #compilation #debugging #how #named

ReduKtor: How We Stopped Worrying About Bugs in Kotlin Compiler (DS, MA, MAB), pp. 317–326.

ASE-2019-AhmedSSK #compilation #fault #generative

Targeted Example Generation for Compilation Errors (UZA, RS, NS, AK), pp. 327–338.

ASE-2019-ChenD0Q #comprehension #debugging #scalability

Understanding Exception-Related Bugs in Large-Scale Cloud Systems (HC, WD, YJ0, FQ), pp. 339–351.

ASE-2019-ZhengLZLZD #detection #feedback #named #online #realtime #scalability

iFeedback: Exploiting User Feedback for Real-Time Issue Detection in Large-Scale Online Service Systems (WZ, HL, YZ, JL, HZ, YD), pp. 352–363.

ASE-2019-ChenHLZHGXDZ #online #scalability

Continuous Incident Triage for Large-Scale Online Service Systems (JC, XH, QL, HZ, DH, FG, ZX, YD, DZ), pp. 364–375.

ASE-2019-ZhangC #adaptation #approach #learning #modelling #named

Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models (HZ, WKC), pp. 376–387.

ASE-2019-HuAMLR #program repair #programming

Re-Factoring Based Program Repair Applied to Programming Assignments (YH, UZA, SM, BL, AR), pp. 388–398.

ASE-2019-EndresSCJW #automation #named

InFix: Automatically Repairing Novice Program Inputs (ME, GS, BC, RJ, WW), pp. 399–410.

ASE-2019-CashinMWF #comprehension #difference #invariant

Understanding Automatically-Generated Patches Through Symbolic Invariant Differences (PC, CM, WW, SF), pp. 411–414.

ASE-2019-MichaelDDLS #programming #regular expression #risk management

Regexes are Hard: Decision-Making, Difficulties, and Risks in Programming Regular Expressions (LGMI, JD, JCD, DL, FS), pp. 415–426.

ASE-2019-DavisMKL #metric #regular expression #scalability #testing

Testing Regex Generalizability And Its Implications: A Large-Scale Many-Language Measurement Study (JCD, DM, AMK, DL), pp. 427–439.

ASE-2019-ShermanH #automaton #constraints #string

Accurate String Constraints Solution Counting with Weighted Automata (ES, AH), pp. 440–452.

ASE-2019-EiersSBB #program analysis

Subformula Caching for Model Counting and Quantitative Program Analysis (WE, SS, TB, TB), pp. 453–464.

ASE-2019-BaoLWF #automation #generative #named #network

ACTGAN: Automatic Configuration Tuning for Software Systems with Generative Adversarial Networks (LB, XL, FW, BF), pp. 465–476.

ASE-2019-HortonP #detection #named #performance #python

V2: Fast Detection of Configuration Drift in Python (EH, CP), pp. 477–488.

ASE-2019-NguyenNTTN #configuration management

Feature-Interaction Aware Configuration Prioritization for Configurable Code (SN, HN, NMT, HT, TNN), pp. 489–501.

ASE-2019-JiangWXCZ #debugging #empirical #fault #locality #statistics

Combining Spectrum-Based Fault Localization and Statistical Debugging: An Empirical Study (JJ, RW, YX, XC, LZ), pp. 502–514.

ASE-2019-ZamanHY #concurrent #fault #named #scalability

SCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces (TSZ, XH, TY), pp. 515–526.

ASE-2019-RenLXJX #analysis #locality

Root Cause Localization for Unreproducible Builds via Causality Analysis Over System Call Tracing (ZR, CL, XX, HJ, TX), pp. 527–538.

ASE-2019-CelikPPAG #analysis #coq #mutation testing

Mutation Analysis for Coq (AÇ, KP, MP, EJGA, MG), pp. 539–551.

ASE-2019-LiuSTWY #c #encryption #source code #verification

Verifying Arithmetic in Cryptographic C Programs (JL, XS, MHT, BYW, BYY), pp. 552–564.

ASE-2019-KimC #embedded #model checking #using

Model Checking Embedded Control Software using OS-in-the-Loop CEGAR (DK, YC), pp. 565–576.

ASE-2019-RecoulesBBMP #assembly

Get Rid of Inline Assembly through Verification-Oriented Lifting (FR, SB, RB, LM, MLP), pp. 577–589.

ASE-2019-Gu00 #api #approach #graph #kernel #named

CodeKernel: A Graph Kernel Based Approach to the Selection of API Usage Examples (XG, HZ0, SK0), pp. 590–601.

ASE-2019-JiangLJ #how #machine learning #recommendation

Machine Learning Based Recommendation of Method Names: How Far are We (LJ, HL, HJ), pp. 602–614.

ASE-2019-NamHMMV #api #identification #mining #named #problem #usability

MARBLE: Mining for Boilerplate Code to Identify API Usability Problems (DN, AH, AM, BAM, BV), pp. 615–627.

ASE-2019-LacomisYSAGNV #approach #identifier #named

DIRE: A Neural Approach to Decompiled Identifier Naming (JL, PY, EJS, MA, CLG, GN, BV), pp. 628–639.

ASE-2019-MuhlbauerAS #evolution #modelling #performance

Accurate Modeling of Performance Histories for Evolving Software Systems (SM, SA, NS), pp. 640–652.

ASE-2019-ChenJML #case study #experience #industrial #refactoring #web

An Industrial Experience Report on Performance-Aware Refactoring on a Database-Centric Web Application (BC, ZMJ, PM, ML), pp. 653–664.

ASE-2019-TaoTLXQ #api #data analysis #how #performance #question #runtime

How Do API Selections Affect the Runtime Performance of Data Analytics Tasks? (YT, ST, YL, ZX, SQ), pp. 665–668.

ASE-2019-ChenSHWL #behaviour #case study #experience #generative #testing #using

An Experience Report of Generating Load Tests Using Log-Recovered Workloads at Varying Granularities of User Behaviour (JC, WS, AEH, YW, JL), pp. 669–681.

ASE-2019-TangZZLXZY #android #library #performance

Demystifying Application Performance Management Libraries for Android (YT, XZ, HZ, XL, ZX, YZ, QY), pp. 682–685.

ASE-2019-LiuHGN #predict #source code

Predicting Licenses for Changed Source Code (XL, LH, JG, VN), pp. 686–697.

ASE-2019-GongJWJ #empirical #evaluation #fault #predict

Empirical Evaluation of the Impact of Class Overlap on Software Defect Prediction (LG, SJ, RW, LJ), pp. 698–709.

ASE-2019-NguyenNLW #program analysis #statistics

Combining Program Analysis and Statistical Language Model for Code Statement Completion (SVN, TNN, YL, SW0), pp. 710–721.

ASE-2019-WangZL00L #named #novel #test coverage #testing #thread

MAP-Coverage: A Novel Coverage Criterion for Testing Thread-Safe Classes (ZW, YZ, SL, JS0, XC0, HL), pp. 722–734.

ASE-2019-ZhangYD #abstraction #automation #concurrent

Automating Non-Blocking Synchronization In Concurrent Data Abstractions (JZ, QY, DD), pp. 735–747.

ASE-2019-WuZ0TZ #automation #program transformation

Automating CUDA Synchronization via Program Transformation (MW, LZ, CL0, SHT, YZ), pp. 748–759.

ASE-2019-PobeeMC #concurrent #multi #performance #source code #thread #transaction

Efficient Transaction-Based Deterministic Replay for Multi-threaded Programs (EBP, XM, WKC), pp. 760–771.

ASE-2019-ZhengFXS0HMLSC #automation #game studies #learning #named #online #testing #using

Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning (YZ, CF, XX, TS, LM0, JH, ZM, YL0, RS, YC), pp. 772–784.

ASE-2019-NejadgholiY #approximate #case study #learning #library #testing

A Study of Oracle Approximations in Testing Deep Learning Libraries (MN, JY0), pp. 785–796.

ASE-2019-GopinathCPT #network

Property Inference for Deep Neural Networks (DG, HC, CSP, AT), pp. 797–809.

ASE-2019-GuoCXMHLLZL #deployment #development #empirical #framework #learning #platform #towards

An Empirical Study Towards Characterizing Deep Learning Development and Deployment Across Different Frameworks and Platforms (QG, SC, XX, LM0, QH, HL, YL0, JZ, XL), pp. 810–822.

ASE-2019-AlizadehOKC #named #refactoring

RefBot: Intelligent Software Refactoring Bot (VA, MAO, MK, MC), pp. 823–834.

ASE-2019-KohlerS #automation #programming #refactoring

Automated Refactoring to Reactive Programming (MK, GS), pp. 835–846.

ASE-2019-VerhaegheFGAD #empirical #interface #programming

Empirical Study of Programming to an Interface (BV, CF, LG, NA, SD), pp. 847–850.

ASE-2019-BaoB0M #difference #statistics

Statistical Log Differencing (LB, NB, DL0, SM), pp. 851–862.

ASE-2019-LiuZHHZL #clustering #named

Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression (JL, JZ, SH, PH, ZZ, MRL), pp. 863–873.

ASE-2019-Boronat #agile #modelling

Code-First Model-Driven Engineering: On the Agile Adoption of MDE Tooling (AB), pp. 874–886.

ASE-2019-BusanyMY #model inference

Size and Accuracy in Model Inference (NB, SM, YY), pp. 887–898.

ASE-2019-PaulsenSPW #named #static analysis

Debreach: Mitigating Compression Side Channels via Static Analysis and Transformation (BP, CS, PAHP, CW), pp. 899–911.

ASE-2019-Nowack #execution #in memory #memory management #representation #symbolic computation

Fine-Grain Memory Object Representation in Symbolic Execution (MN), pp. 912–923.

ASE-2019-MuGCCGXMS #alias #analysis #execution #named #performance

RENN: Efficient Reverse Execution with Neural-Network-Assisted Alias Analysis (DM, WG, AC, YC, JG, XX, BM, CS), pp. 924–935.

ASE-2019-VeduradaN #alias #analysis

Batch Alias Analysis (JV, VKN), pp. 936–948.

ASE-2019-PalmerinoYDK #adaptation #process #self

Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility (JP, QY, TD, DEK), pp. 949–961.

ASE-2019-ChenPSAZ #cyber-physical #fuzzing #network #testing

Learning-Guided Network Fuzzing for Testing Cyber-Physical System Defences (YC, CMP, JS, SA, FZ), pp. 962–973.

ASE-2019-MaiaVCYZN #adaptation #component

Cautious Adaptation of Defiant Components (PHMM, LV, MC, YY, AZ, BN), pp. 974–985.

ASE-2019-FengCKC0F #evolution #monitoring

Active Hotspot: An Issue-Oriented Model to Monitor Software Evolution and Degradation (QF, YC, RK, DC, TL0, HF), pp. 986–997.

ASE-2019-Gerostathopoulos #automation #evaluation

Automated Trainability Evaluation for Smart Software Functions (IG, SK, CS, TB, AK), pp. 998–1001.

ASE-2019-CavalcantiBSA

The Impact of Structure on Software Merging: Semistructured Versus Structured Merge (GC, PB, GS, SA), pp. 1002–1013.

ASE-2019-TavaresBCS #javascript

Semistructured Merge in JavaScript Systems (ATT, PB, GC, SS), pp. 1014–1025.

ASE-2019-NafiKRRS #api #clone detection #detection #documentation #named #using

CLCDSA: Cross Language Code Clone Detection using Syntactical Features and API Documentation (KWN, TSK, BR, CKR, KAS), pp. 1026–1037.

ASE-2019-FengYLBWTSYXPXH #detection #named #off the shelf #open source #reuse

B2SFinder: Detecting Open-Source Software Reuse in COTS Software (MF, ZY, FL, GB, SW, QT, HS, CY, JX, AP, JX, WH), pp. 1038–1049.

ASE-2019-WangLZX #co-evolution #named

CoRA: Decomposing and Describing Tangled Code Changes for Reviewer (MW, ZL, YZ, BX), pp. 1050–1061.

ASE-2019-DuX000Z #analysis #framework #network

A Quantitative Analysis Framework for Recurrent Neural Network (XD, XX, YL0, LM0, YL0, JZ), pp. 1062–1065.

ASE-2019-YuF0Z0 #automation #image #layout #mobile #named #platform #recognition #testing

LIRAT: Layout and Image Recognition Driving Automated Mobile Testing of Cross-Platform (SY, CF, YF0, WZ, ZC0), pp. 1066–1069.

ASE-2019-LiY0C #android #approach #automation #black box #named #testing

Humanoid: A Deep Learning-Based Approach to Automated Black-box Android App Testing (YL, ZY, YG0, XC), pp. 1070–1073.

ASE-2019-BeyerL #execution #metric #named #robust #testing

TestCov: Robust Test-Suite Execution and Coverage Measurement (DB0, TL0), pp. 1074–1077.

ASE-2019-ZhouWLLS0 #comprehension #fuzzing #interactive #named #visualisation

VisFuzz: Understanding and Intervening Fuzzing with Interactive Visualization (CZ, MW, JL, ZL, CS, YJ0), pp. 1078–1081.

ASE-2019-AmreenKM #developer

Developer Reputation Estimator (DRE) (SA, AK, AM), pp. 1082–1085.

ASE-2019-DuCWLSC #graph #named

CocoQa: Question Answering for Coding Conventions Over Knowledge Graphs (TD, JC, QW, WL, BS, YC), pp. 1086–1089.

ASE-2019-Escobar-Velasquez #android #generative #named

MutAPK: Source-Codeless Mutant Generation for Android Apps (CEV, MOR, MLV), pp. 1090–1093.

ASE-2019-PiskachevDJB #automation #detection #named

SWAN_ASSIST: Semi-Automated Detection of Code-Specific, Security-Relevant Methods (GP, LNQD, OJ, EB), pp. 1094–1097.

ASE-2019-SadiqLLAL #contract #java #named #source code

Sip4J: Statically Inferring Access Permission Contracts for Parallelising Sequential Java Programs (AS, LL, YFL, IA, SL), pp. 1098–1101.

ASE-2019-ArthoPT #concurrent #java #visual notation

Visual Analytics for Concurrent Java Executions (CA, MP, QT0), pp. 1102–1105.

ASE-2019-ZhangYFSL0 #learning #modelling #named #visualisation

NeuralVis: Visualizing and Interpreting Deep Learning Models (XZ, ZY, YF0, QS, JL, ZC0), pp. 1106–1109.

ASE-2019-TankovGB #framework #named

Kotless: A Serverless Framework for Kotlin (VT, YG, TB), pp. 1110–1113.

ASE-2019-LiuFXLGGY #automation #evaluation #named #performance #simulation #tool support #workflow

FogWorkflowSim: An Automated Simulation Toolkit for Workflow Performance Evaluation in Fog Computing (XL, LF, JX, XL, LG, JCG, YY), pp. 1114–1117.

ASE-2019-GhanbariZ #bytecode #named #program repair

PraPR: Practical Program Repair via Bytecode Mutation (AG, LZ), pp. 1118–1121.

ASE-2019-MaYLYZ #c++ #fault #named #pointer #source code #static analysis

SPrinter: A Static Checker for Finding Smart Pointer Errors in C++ Programs (XM, JY, YL, JY0, JZ), pp. 1122–1125.

ASE-2019-Laguna #detection #exception #float #gpu #named

FPChecker: Detecting Floating-Point Exceptions in GPU Applications (IL), pp. 1126–1129.

ASE-2019-CastroPA #locality #named #tool support

Pangolin: An SFL-Based Toolset for Feature Localization (BC, AP, RA), pp. 1130–1133.

ASE-2019-ReulingKRL #analysis #configuration management

SiMPOSE - Configurable N-Way Program Merging Strategies for Superimposition-Based Analysis of Variant-Rich Software (DR, UK, SR, ML), pp. 1134–1137.

ASE-2019-AfzalACCDDKV #abstraction #generative #testing #verification

VeriAbs : Verification by Abstraction and Test Generation (MA, AA, AC, BC, PD, AD, SK, RV), pp. 1138–1141.

ASE-2019-LiW0ZCM #clustering #detection #effectiveness #fault #named #spreadsheet

SGUARD: A Feature-Based Clustering Tool for Effective Spreadsheet Defect Detection (DL, HW, CX0, RZ, SCC, XM), pp. 1142–1145.

ASE-2019-ReicheltKH #identification #named #performance

PeASS: A Tool for Identifying Performance Changes at Code Level (DGR, SK, WH), pp. 1146–1149.

ASE-2019-FischerTP #concurrent #multi #source code #thread

VeriSmart 2.0: Swarm-Based Bug-Finding for Multi-threaded Programs with Lazy-CSeq (BF, SLT, GP), pp. 1150–1153.

ASE-2019-MengZYL0Y #concurrent #detection #effectiveness #named

CONVUL: An Effective Tool for Detecting Concurrency Vulnerabilities (RM, BZ, HY, HL, YC0, ZY), pp. 1154–1157.

ASE-2019-Hu0XY0Z #framework #learning #mutation testing #testing

DeepMutation++: A Mutation Testing Framework for Deep Learning Systems (QH, LM0, XX, BY, YL0, JZ), pp. 1158–1161.

ASE-2019-XieCLM0Z #fuzzing #network

Coverage-Guided Fuzzing for Feedforward Neural Networks (XX, HC, YL0, LM0, YL0, JZ), pp. 1162–1165.

ASE-2019-HuangFZZWJMP #analysis #editing #modelling #named #precise #requirements

Prema: A Tool for Precise Requirements Editing, Modeling and Analysis (YH, JF, HZ, JZ, SW, SJ, WM, GP), pp. 1166–1169.

ASE-2019-WangC00S #analysis #fault #memory management #named #pointer

TsmartGP: A Tool for Finding Memory Defects with Pointer Analysis (YW, GC, MZ0, MG0, JS), pp. 1170–1173.

ASE-2019-Li0G00 #fault #named #specification #static analysis

Ares: Inferring Error Specifications through Static Analysis (CL, MZ0, ZG, MG0, HZ0), pp. 1174–1177.

ASE-2019-BagherzadehJKD #execution #modelling #named #uml

PMExec: An Execution Engine of Partial UML-RT Models (MB, KJ, NK, JD), pp. 1178–1181.

ASE-2019-AhmadiJD #named #state machine #testing #uml

mCUTE: A Model-Level Concolic Unit Testing Engine for UML State Machines (RA, KJ, JD), pp. 1182–1185.

ASE-2019-MossbergMHGGFBD #contract #execution #framework #named #symbolic computation

Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts (MM, FM, EH, AG, GG, JF, TB, AD), pp. 1186–1189.

ASE-2019-ChittimalliAPMP #framework #named #verification

BuRRiTo: A Framework to Extract, Specify, Verify and Analyze Business Rules (PKC, KA, SP, SM, CP, RS, RN), pp. 1190–1193.

ASE-2019-MehraSKP #artificial reality #named #towards #using

XRaSE: Towards Virtually Tangible Software using Augmented Reality (RM, VSS, VK, SP), pp. 1194–1197.

ASE-2019-LiWXWZ0 #contract #mutation testing #named #testing

MuSC: A Tool for Mutation Testing of Ethereum Smart Contract (ZL, HW, JX, XW, LZ, ZC0), pp. 1198–1201.

ASE-2019-ZhouSZ #named #what

Lancer: Your Code Tell Me What You Need (SZ, BS, HZ), pp. 1202–1205.

ASE-2019-TokumotoT #development #metric #named #quality

PHANTA: Diversified Test Code Quality Measurement for Modern Software Development (ST, KT), pp. 1206–1207.

ASE-2019-SungKKJK #automation #case study #testing

Test Automation and Its Limitations: A Case Study (AS, SK, YK, YJ, JK), pp. 1208–1209.

ASE-2019-WenCC #kernel #linux #named

PTracer: A Linux Kernel Patch Trace Bot (YW, JC, SC), pp. 1210–1211.

ASE-2019-SingiBPB

Trusted Software Supply Chain (KS, RPJCB, SP, APB), pp. 1212–1213.

ASE-2019-SharmaMPB #delivery #development #towards

A Journey Towards Providing Intelligence and Actionable Insights to Development Teams in Software Delivery (VSS, RM, SP, APB), pp. 1214–1215.

ASE-2019-Wu0C #case study #development #experience #safety

Better Development of Safety Critical Systems: Chinese High Speed Railway System Development Experience Report (ZW, JL0, XC), pp. 1216–1217.

ASE-2019-Zhou #collaboration #development #performance

Improving Collaboration Efficiency in Fork-Based Development (SZ), pp. 1218–1221.

ASE-2019-Reich #automation #requirements #verification

Inference of Properties from Requirements and Automation of Their Formal Verification (MR), pp. 1222–1225.

ASE-2019-Lukasczyk #dynamic typing #generative #source code #testing

Generating Tests to Analyse Dynamically-Typed Programs (SL), pp. 1226–1229.

ASE-2019-Soto #automation #component #program repair #quality

Improving Patch Quality by Enhancing Key Components of Automatic Program Repair (MS), pp. 1230–1233.

ASE-2019-Kolthoff #automation #generative #natural language #prototype #requirements #strict #user interface #visual notation

Automatic Generation of Graphical User Interface Prototypes from Unrestricted Natural Language Requirements (KK), pp. 1234–1237.

ASE-2019-Sharma #adaptation #automation #source code #synthesis #using

Automatically Repairing Binary Programs Using Adapter Synthesis (VS), pp. 1238–1241.

ASE-2019-Hassan #integration

Tackling Build Failures in Continuous Integration (FH), pp. 1242–1245.

ASE-2019-Vassallo #integration #process

Enabling Continuous Improvement of a Continuous Integration Process (CV), pp. 1246–1249.

ASE-2019-Wei #generative

Retrieve and Refine: Exemplar-Based Neural Comment Generation (BW), pp. 1250–1252.

ASE-2019-Nam #api #design

API Design Implications of Boilerplate Client Code (DN), pp. 1253–1255.

ASE-2019-Kellogg #detection #image

Compile-Time Detection of Machine Image Sniping (MK), pp. 1256–1258.

ASE-2019-Xiao #android #approach #detection

An Image-Inspired and CNN-Based Android Malware Detection Approach (XX), pp. 1259–1261.

ASE-2019-Ghanbari #automation #program repair #towards

Toward Practical Automatic Program Repair (AG), pp. 1262–1264.

ASE-2019-Ramamoorthy #game studies #multi #using

User Preference Aware Multimedia Pricing Model using Game Theory and Prospect Theory for Wireless Communications (KMKR), pp. 1265–1267.

ASE-2019-Neupane #approach #development

An Approach for Investigating Emotion Dynamics in Software Development (KN), pp. 1268–1270.

ASE-2019-Mudduluru #source code #verification

Verifying Determinism in Sequential Programs (RM), pp. 1271–1273.

ASE-2019-Yu #empirical #graph #python

Empirical Study of Python Call Graph (LY), pp. 1274–1276.

ASE-2019-Yu19a #comprehension #crowdsourcing #debugging #generative

Crowdsourced Report Generation via Bug Screenshot Understanding (SY), pp. 1277–1279.

ASE-2019-Jiang #analysis #commit #generative #semantics

Boosting Neural Commit Message Generation with Code Semantic Analysis (SJ), pp. 1280–1282.

ASE-2019-Balasubramaniam #machine learning #representation #towards #using

Towards Comprehensible Representation of Controllers using Machine Learning (GB), pp. 1283–1285.

ASE-2019-Zhang #approach #identification #injection #machine learning #sql

A Machine Learning Based Approach to Identify SQL Injection Vulnerabilities (KZ), pp. 1286–1288.