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

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@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 (, 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.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.