Proceedings of the 13th Joint Meeting of the 18th European Software Engineering Conference and the 27th Symposium on the Foundations of Software Engineering
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Marlon Dumas, Dietmar Pfahl, Sven Apel, Alessandra Russo
Proceedings of the 13th Joint Meeting of the 18th European Software Engineering Conference and the 27th Symposium on the Foundations of Software Engineering
ESEC/FSE, 2019.

SE
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@proceedings{ESEC-FSE-2019,
	acmid         = "3338906",
	editor        = "Marlon Dumas and Dietmar Pfahl and Sven Apel and Alessandra Russo",
	isbn          = "978-1-4503-5572-8",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 13th Joint Meeting of the 18th European Software Engineering Conference and the 27th Symposium on the Foundations of Software Engineering}",
	year          = 2019,
}

Contents (150 items)

ESEC-FSE-2019-Atlee #feature model #interactive
Living with feature interactions (keynote) (JMA), p. 1.
ESEC-FSE-2019-Kwiatkowska #learning #robust #safety
Safety and robustness for deep learning with provable guarantees (keynote) (MK), p. 2.
ESEC-FSE-2019-Mockus #open source
Insights from open source software supply chains (keynote) (AM), p. 3.
ESEC-FSE-2019-AhmadiD #modelling #testing
Concolic testing for models of state-based systems (RA, JD), pp. 4–15.
ESEC-FSE-2019-KimHK #composition #debugging #detection #effectiveness #refinement #summary #testing
Target-driven compositional concolic testing with function summary refinement for effective bug detection (YK, SH, MK), pp. 16–26.
ESEC-FSE-2019-MenghiNGB #automation #behaviour #generative #modelling #nondeterminism #online #testing
Generating automated and online test oracles for Simulink models with continuous and uncertain behaviors (CM, SN, KG, LCB), pp. 27–38.
ESEC-FSE-2019-ShahinCS #analysis #product line
Lifting Datalog-based analyses to software product lines (RS, MC, RS), pp. 39–49.
ESEC-FSE-2019-MordahlOKWG #debugging #detection #empirical #tool support #variability
An empirical study of real-world variability bugs detected by variability-oblivious tools (AM, JO, UK, SW, PG), pp. 50–61.
ESEC-FSE-2019-NesicKSB #feature model #modelling
Principles of feature modeling (DN, JK, SS, TB), pp. 62–73.
ESEC-FSE-2019-RiggerMAM #comprehension #tool support
Understanding GCC builtins to develop better tools (MR, SM, BA, HM), pp. 74–85.
ESEC-FSE-2019-ChaparroBLMMPPN #debugging #quality
Assessing the quality of the steps to reproduce in bug reports (OC, CBC, JL, KM, AM, MDP, DP, VN), pp. 86–96.
ESEC-FSE-2019-Wang0LXBXW #approach #automation #documentation #source code #taxonomy
A learning-based approach for automatic construction of domain glossary from source code and documentation (CW, XP0, ML, ZX, XB, BX, TW), pp. 97–108.
ESEC-FSE-2019-FucciMM #api #documentation #identification #machine learning #on the #using
On using machine learning to identify knowledge in API reference documentation (DF, AM, WM), pp. 109–119.
ESEC-FSE-2019-Liu0MXXXL #api #generative #summary
Generating query-specific class API summaries (ML, XP0, AM, ZX, WX, SX, YL), pp. 120–130.
ESEC-FSE-2019-JiangLZ #semantics
Semantic relation based expansion of abbreviations (YJ, HL, LZ), pp. 131–141.
ESEC-FSE-2019-BiagiolaSRT #generative #testing #web
Diversity-based web test generation (MB, AS, FR, PT), pp. 142–153.
ESEC-FSE-2019-BiagiolaSMRT #dependence #detection #web
Web test dependency detection (MB, AS, AM, FR, PT), pp. 154–164.
ESEC-FSE-2019-StahlbauerKF #automation #source code #testing
Testing scratch programs automatically (AS, MK, GF), pp. 165–175.
ESEC-FSE-2019-Zhang0C0Z #compilation #empirical #fault #integration #scalability
A large-scale empirical study of compiler errors in continuous integration (CZ, BC0, LC, XP0, WZ), pp. 176–187.
ESEC-FSE-2019-HeMS0PS #performance #statistics #testing
A statistics-based performance testing methodology for cloud applications (SH, GM, JS, WW0, LLP, MLS), pp. 188–199.
ESEC-FSE-2019-CotroneoSLNB #analysis #debugging #empirical #framework #how #in the cloud #platform
How bad can a bug get? an empirical analysis of software failures in the OpenStack cloud computing platform (DC, LDS, PL, RN, NB), pp. 200–211.
ESEC-FSE-2019-LinCLLZ #algorithm #combinator #generative #metaheuristic #performance #testing #towards
Towards more efficient meta-heuristic algorithms for combinatorial test generation (JL, SC, CL, QL, HZ0), pp. 212–222.
ESEC-FSE-2019-ChenHSZHZ #compilation #debugging #effectiveness #generative
Compiler bug isolation via effective witness test program generation (JC, JH, PS, LZ, DH, LZ0), pp. 223–234.
ESEC-FSE-2019-ChaO #adaptation #heuristic #testing
Concolic testing with adaptively changing search heuristics (SC, HO), pp. 235–245.
ESEC-FSE-2019-BaresiDQ #execution #parallel #symbolic computation
Symbolic execution-driven extraction of the parallel execution plans of Spark applications (LB, GD, GQ), pp. 246–256.
ESEC-FSE-2019-GambiHF #effectiveness #generative #self #testing
Generating effective test cases for self-driving cars from police reports (AG, TH, GF), pp. 257–267.
ESEC-FSE-2019-LuPZ0L #android #testing
Preference-wise testing for Android applications (YL, MP, JZ, TZ0, XL), pp. 268–278.
ESEC-FSE-2019-NajafiRS #commit #modelling #testing
Bisecting commits and modeling commit risk during testing (AN, PCR, WS), pp. 279–289.
ESEC-FSE-2019-GulzarMMK #big data #data analysis #testing
White-box testing of big data analytics with complex user-defined functions (MAG, SM, MM, MK), pp. 290–301.
ESEC-FSE-2019-DurieuxDMA #debugging #empirical #java #overview #program repair #scalability #tool support
Empirical review of Java program repair tools: a large-scale experiment on 2, 141 bugs and 23, 551 repair attempts (TD, FM, MM, RA), pp. 302–313.
ESEC-FSE-2019-KoyuncuLB0MKT #debugging #named #program repair
iFixR: bug report driven program repair (AK, KL0, TFB, DK0, MM, JK, YLT), pp. 314–325.
ESEC-FSE-2019-WenWLTXCS #commit #correlation #debugging
Exploring and exploiting the correlations between bug-inducing and bug-fixing commits (MW, RW, YL, YT, XX, SCC, ZS), pp. 326–337.
ESEC-FSE-2019-KrugerCBLS #comprehension #traceability
Effects of explicit feature traceability on program comprehension (JK, , TB, TL, GS), pp. 338–349.
ESEC-FSE-2019-ZhouVK #case study #performance #social #what
What the fork: a study of inefficient and efficient forking practices in social coding (SZ, BV, CK), pp. 350–361.
ESEC-FSE-2019-SongZH #android #detection #named
ServDroid: detecting service usage inefficiencies in Android applications (WS0, JZ, JH0), pp. 362–373.
ESEC-FSE-2019-PauckW #analysis #android
Together strong: cooperative Android app analysis (FP, HW), pp. 374–384.
ESEC-FSE-2019-NieRLKMG #execution #framework
A framework for writing trigger-action todo comments in executable format (PN, RR, JJL, SK, RJM, MG), pp. 385–396.
ESEC-FSE-2019-SafwanS #commit #developer #perspective
Decomposing the rationale of code commits: the software developer's perspective (KAS, FS), pp. 397–408.
ESEC-FSE-2019-MollerT #library #modelling #testing
Model-based testing of breaking changes in Node.js libraries (AM, MTT), pp. 409–419.
ESEC-FSE-2019-WinterACD #ide
Monitoring-aware IDEs (JW, MFA, JC, AvD), pp. 420–431.
ESEC-FSE-2019-BagherzadehK #big data #developer #scalability #what
Going big: a large-scale study on what big data developers ask (MB, RK), pp. 432–442.
ESEC-FSE-2019-DavisMCSL #empirical #regular expression #why
Why aren't regular expressions a lingua franca? an empirical study on the re-use and portability of regular expressions (JCD, LGMI, CAC, FS, DL), pp. 443–454.
ESEC-FSE-2019-NielsenHG #named #static analysis
Nodest: feedback-driven static analysis of Node.js applications (BBN, BH, FG), pp. 455–465.
ESEC-FSE-2019-DeFreezBRT #effectiveness
Effective error-specification inference via domain-knowledge expansion (DD, HMB, CRG, AVT), pp. 466–476.
ESEC-FSE-2019-DuXLM0Z #analysis #learning #modelling #named
DeepStellar: model-based quantitative analysis of stateful deep learning systems (XD, XX, YL0, LM0, YL0, JZ), pp. 477–487.
ESEC-FSE-2019-WuJYBSPX #grammar inference #learning #named
REINAM: reinforcement learning for input-grammar inference (ZW, EJ, WY0, OB, DS, JP, TX0), pp. 488–498.
ESEC-FSE-2019-LiM0CX0 #performance #testing
Boosting operational DNN testing efficiency through conditioning (ZL, XM, CX0, CC, JX0, JL0), pp. 499–509.
ESEC-FSE-2019-IslamNPR #debugging #learning
A comprehensive study on deep learning bug characteristics (MJI, GN, RP, HR), pp. 510–520.
ESEC-FSE-2019-LiewCDS #constraints #float #fuzzing #using
Just fuzz it: solving floating-point constraints using coverage-guided fuzzing (DL, CC, AFD, JRS), pp. 521–532.
ESEC-FSE-2019-LiXCWZXWL #adaptation #detection #effectiveness #fuzzing #named
Cerebro: context-aware adaptive fuzzing for effective vulnerability detection (YL, YX, HC, XW, CZ, XX, HW, YL0), pp. 533–544.
ESEC-FSE-2019-ShiLOXM #automation #framework #named #testing
iFixFlakies: a framework for automatically fixing order-dependent flaky tests (AS, WL, RO, TX, DM), pp. 545–555.
ESEC-FSE-2019-KalhaugeP #dependence #graph #reduction
Binary reduction of dependency graphs (CGK, JP), pp. 556–566.
ESEC-FSE-2019-PobeeC #concurrent #multi #named #performance #source code #thread
AggrePlay: efficient record and replay of multi-threaded programs (EBP, WKC), pp. 567–577.
ESEC-FSE-2019-HiraoMIM #approach #code review #empirical #graph #overview
The review linkage graph for code review analytics: a recovery approach and empirical study (TH, SM, AI, KM), pp. 578–589.
ESEC-FSE-2019-WangSW #compilation
Mitigating power side channels during compilation (JW, CS, CW0), pp. 590–601.
ESEC-FSE-2019-ChenMF #multi #specification #synthesis
Maximal multi-layer specification synthesis (YC, RM, YF), pp. 602–612.
ESEC-FSE-2019-BavishiYP #automation #data-driven #named #static analysis #synthesis
Phoenix: automated data-driven synthesis of repairs for static analysis violations (RB, HY, MRP), pp. 613–624.
ESEC-FSE-2019-AggarwalLNDS #black box #machine learning #modelling #testing
Black box fairness testing of machine learning models (AA, PL, SN, KD, DS), pp. 625–635.
ESEC-FSE-2019-PontesGSGR #api #java
Java reflection API: revealing the dark side of the mirror (FP, RG, SS, AG, MR), pp. 636–646.
ESEC-FSE-2019-WidderHKV #concept #integration #replication
A conceptual replication of continuous integration pain points in the context of Travis CI (DGW, MH, CK, BV), pp. 647–658.
ESEC-FSE-2019-ZhangHZHB #overview #re-engineering #research
Ethnographic research in software engineering: a critical review and checklist (HZ, XH, XZ, HH, MAB), pp. 659–670.
ESEC-FSE-2019-SantosSCGM #approach #architecture
Achilles' heel of plug-and-Play software architectures: a grounded theory based approach (JCSS, AS, TC, SG, MM), pp. 671–682.
ESEC-FSE-2019-Zhou0X0JLXH #fault #learning #locality #predict
Latent error prediction and fault localization for microservice applications by learning from system trace logs (XZ, XP0, TX, JS0, CJ, DL, QX, CH), pp. 683–694.
ESEC-FSE-2019-JimenezRPSTH #predict
The importance of accounting for real-world labelling when predicting software vulnerabilities (MJ, RR, MP, FS, YLT, MH), pp. 695–705.
ESEC-FSE-2019-CaiZMYHSL #concurrent #detection #memory management
Detecting concurrency memory corruption vulnerabilities (YC, BZ, RM, HY, LH, PS, BL0), pp. 706–717.
ESEC-FSE-2019-WangXLLLQLL #layout #memory management
Locating vulnerabilities in binaries via memory layout recovering (HW, XX, SWL, YL0, YL, SQ, YL0, TL0), pp. 718–728.
ESEC-FSE-2019-DuttaZHM #debugging #named #probability #programming #reduction #testing
Storm: program reduction for testing and debugging probabilistic programming systems (SD, WZ, ZH, SM), pp. 729–739.
ESEC-FSE-2019-BanerjeeCS #java #named #null #safety #type system
NullAway: practical type-based null safety for Java (SB, LC, MS), pp. 740–750.
ESEC-FSE-2019-JiaLYLW #automation #detection
Automatically detecting missing cleanup for ungraceful exits (ZJ, SL, TY, XL, JW), pp. 751–762.
ESEC-FSE-2019-ZhangSYZPS #comprehension #debugging #model checking
Finding and understanding bugs in software model checkers (CZ, TS, YY, FZ, GP, ZS), pp. 763–773.
ESEC-FSE-2019-KapusC #execution #memory management #symbolic computation
A segmented memory model for symbolic execution (TK, CC), pp. 774–784.
ESEC-FSE-2019-KulaRHDG #case study #performance
Releasing fast and slow: an exploratory case study at ING (EK, AR, HH, AvD, GG), pp. 785–795.
ESEC-FSE-2019-BuiYJ #api #learning #named
SAR: learning cross-language API mappings with little knowledge (NDQB, YY, LJ), pp. 796–806.
ESEC-FSE-2019-ZhangXLQZDXYCLC #detection #robust
Robust log-based anomaly detection on unstable log data (XZ, YX, QL, BQ, HZ0, YD, CX, XY, QC, ZL, JC0, XH, RY, JGL, MC, FS, DZ), pp. 807–817.
ESEC-FSE-2019-SuWC0 #java #performance
Pinpointing performance inefficiencies in Java (PS, QW, MC, XL0), pp. 818–829.
ESEC-FSE-2019-EckPCB #comprehension #developer #perspective #testing
Understanding flaky tests: the developer's perspective (ME, FP, MC, AB), pp. 830–840.
ESEC-FSE-2019-ChenCLML #analysis #approach #learning #named #re-engineering #sentiment
SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering (ZC, YC, XL, QM, XL), pp. 841–852.
ESEC-FSE-2019-JinWXPDQ0X #generative #named #testing
FinExpert: domain-specific test generation for FinTech systems (TJ, QW, LX, CP, LD, HQ, LH0, TX), pp. 853–862.
ESEC-FSE-2019-LohiaKSM #design #diagrams #ontology
Design diagrams as ontological source (PL, KK, BS, SM), pp. 863–873.
ESEC-FSE-2019-MaddilaBN #case study #predict #scalability
Predicting pull request completion time: a case study on large scale cloud services (CSM, CB, NN), pp. 874–882.
ESEC-FSE-2019-YuFMRPC #automation #named #testing #user interface
TERMINATOR: better automated UI test case prioritization (ZY0, FMF, TM, GR, KP, SC), pp. 883–894.
ESEC-FSE-2019-OlssonF #ecosystem #industrial #mining #risk management
Risks and assets: a qualitative study of a software ecosystem in the mining industry (TO, UF), pp. 895–904.
ESEC-FSE-2019-NguyenSCMBL #as a service #multitenancy #using
Using microservices for non-intrusive customization of multi-tenant SaaS (PHN, HS, FC, RM, SB, EL), pp. 905–915.
ESEC-FSE-2019-ChenCCHCM #predict
Predicting breakdowns in cloud services (with SPIKE) (JC, JC, PC, KH, SC, TM), pp. 916–924.
ESEC-FSE-2019-MesbahRJGA #compilation #fault #learning #named
DeepDelta: learning to repair compilation errors (AM, AR, EJ, NG, EA), pp. 925–936.
ESEC-FSE-2019-AsthanaKBBBMMA #automation #named #scalability
WhoDo: automating reviewer suggestions at scale (SA, RK0, RB, CB, CB, CSM, SM, BA), pp. 937–945.
ESEC-FSE-2019-MiryeganehAH #approach #automation #dataset #integration #towards
An IR-based approach towards automated integration of geo-spatial datasets in map-based software systems (NM, MA, HH), pp. 946–954.
ESEC-FSE-2019-IvankovicPJF #test coverage
Code coverage at Google (MI, GP, RJ, GF), pp. 955–963.
ESEC-FSE-2019-CambroneroLKS0 #code search #learning
When deep learning met code search (JC, HL, SK, KS, SC0), pp. 964–974.
ESEC-FSE-2019-BabicBCIKKLSW #generative #named #scalability
FUDGE: fuzz driver generation at scale (DB, SB, YC, FI, TK, MK, CL, LS, WW), pp. 975–985.
ESEC-FSE-2019-ShiWFWSJSJS #enterprise #fuzzing #industrial #kernel #linux
Industry practice of coverage-guided enterprise Linux kernel fuzzing (HS, RW, YF, MW, XS, XJ, HS, YJ0, JS), pp. 986–995.
ESEC-FSE-2019-RueckertBKSMF #architecture #case study #experience #industrial
Architectural decision forces at work: experiences in an industrial consultancy setting (JR, AB, HK, TS, AM, CF), pp. 996–1005.
ESEC-FSE-2019-Gamez-Diaz0RMKB #api #industrial
The role of limitations and SLAs in the API industry (AGD, PF0, ARC, PJM, NK, PB, MM, FM), pp. 1006–1014.
ESEC-FSE-2019-NejatiGMBFW #model checking #modelling #requirements #testing
Evaluating model testing and model checking for finding requirements violations in Simulink models (SN, KG, CM, LCB, SF, DW), pp. 1015–1025.
ESEC-FSE-2019-LangP #c++ #case study #framework #model checking
Model checking a C++ software framework: a case study (JL, ISWBP), pp. 1026–1036.
ESEC-FSE-2019-Morales-Trujillo #case study #evolution #experience
Evolving with patterns: a 31-month startup experience report (MEMT, GAGM), pp. 1037–1047.
ESEC-FSE-2019-BarashFJRTZ #feature model #interactive #ml #requirements #using
Bridging the gap between ML solutions and their business requirements using feature interactions (GB, EF, IJ, OR, RTB, MZ), pp. 1048–1058.
ESEC-FSE-2019-DobrigkeitP #comprehension #design #re-engineering
Design thinking in practice: understanding manifestations of design thinking in software engineering (FD, DdP), pp. 1059–1069.
ESEC-FSE-2019-CorreiaASN #multi #named #testing #using
MOTSD: a multi-objective test selection tool using test suite diagnosability (DC, RA, PS, JN), pp. 1070–1074.
ESEC-FSE-2019-CaiWH0X0 #api #named #recommendation
BIKER: a tool for Bi-information source based API method recommendation (LC, HW, QH, XX0, ZX, DL0), pp. 1075–1079.
ESEC-FSE-2019-ChekamPT #generative #named
Mart: a mutant generation tool for LLVM (TTC, MP, YLT), pp. 1080–1084.
ESEC-FSE-2019-TundoMORGM #as a service #framework #modelling #named
VARYS: an agnostic model-driven monitoring-as-a-service framework for the cloud (AT, MM, MO, OR, MG, LM), pp. 1085–1089.
ESEC-FSE-2019-StallenbergP #detection #injection #named #search-based #web #xml
JCOMIX: a search-based tool to detect XML injection vulnerabilities in web applications (DMS, AP), pp. 1090–1094.
ESEC-FSE-2019-SuiZZZX #analysis #android #debugging #difference #effectiveness #reduction #user interface
Event trace reduction for effective bug replay of Android apps via differential GUI state analysis (YS, YZ0, WZ, MZ, JX), pp. 1095–1099.
ESEC-FSE-2019-AnBPY #framework #independence #search-based
PyGGI 2.0: language independent genetic improvement framework (GA, AB, JP, SY), pp. 1100–1104.
ESEC-FSE-2019-MostaeenSRRS #machine learning #named #validation
CloneCognition: machine learning based code clone validation tool (GM, JS, BR, CKR, KAS), pp. 1105–1109.
ESEC-FSE-2019-FuRMSYJLS #detection #named #testing
EVMFuzzer: detect EVM vulnerabilities via fuzz testing (YF, MR, FM, HS, XY, YJ0, HL, XS), pp. 1110–1114.
ESEC-FSE-2019-FuC #distributed
A dynamic taint analyzer for distributed systems (XF, HC), pp. 1115–1119.
ESEC-FSE-2019-Gamez-Diaz0R #api #ecosystem
Governify for APIs: SLA-driven ecosystem for API governance (AGD, PF0, ARC), pp. 1120–1123.
ESEC-FSE-2019-AtzeiBLYZ #contract
Developing secure bitcoin contracts with BitML (NA, MB, SL, NY, RZ), pp. 1124–1128.
ESEC-FSE-2019-AwadhutkarSHK #algorithm #complexity #detection #named
DISCOVER: detecting algorithmic complexity vulnerabilities (PA, GRS, BH, SK), pp. 1129–1133.
ESEC-FSE-2019-CaiWXH00X #generative #named #stack overflow #summary
AnswerBot: an answer summary generation tool based on stack overflow (LC, HW, BX, QH, XX0, DL0, ZX), pp. 1134–1138.
ESEC-FSE-2019-GuerreroFJF0MR #agile #development #framework #named
Eagle: a team practices audit framework for agile software development (AG, RF, AJ, AF, PF0, CM, ARC), pp. 1139–1143.
ESEC-FSE-2019-Caulo #fault #metric #predict #taxonomy
A taxonomy of metrics for software fault prediction (MC), pp. 1144–1147.
ESEC-FSE-2019-Coviello #distributed #execution #integration #testing
Distributed execution of test cases and continuous integration (CC), pp. 1148–1151.
ESEC-FSE-2019-Denkers #case study #domain-specific language #industrial #using
A longitudinal field study on creation and use of domain-specific languages in industry (JD), pp. 1152–1155.
ESEC-FSE-2019-Ginelli #program repair
Failure-driven program repair (DG), pp. 1156–1159.
ESEC-FSE-2019-Greiner #model transformation #on the #product line #reuse
On extending single-variant model transformations for reuse in software product line engineering (SG), pp. 1160–1163.
ESEC-FSE-2019-Karlsson
Exploratory test agents for stateful software systems (SK), pp. 1164–1167.
ESEC-FSE-2019-Marques #developer #documentation #natural language
Helping developers search and locate task-relevant information in natural language documents (AM), pp. 1168–1171.
ESEC-FSE-2019-Melegati #requirements
Improving requirements engineering practices to support experimentation in software startups (JM), pp. 1172–1175.
ESEC-FSE-2019-Muller
Managing the open cathedral (MM0), pp. 1176–1179.
ESEC-FSE-2019-Sonnekalb #detection #source code
Machine-learning supported vulnerability detection in source code (TS), pp. 1180–1183.
ESEC-FSE-2019-Papachristou #clustering #graph #semantics
Software clusterings with vector semantics and the call graph (MP), pp. 1184–1186.
ESEC-FSE-2019-Moghadam #machine learning #performance #testing
Machine learning-assisted performance testing (MHM), pp. 1187–1189.
ESEC-FSE-2019-Stepanov
File tracing by intercepting disk requests (VS), pp. 1190–1192.
ESEC-FSE-2019-Abid #api #recommendation
Recommending related functions from API usage-based function clone structures (SA), pp. 1193–1195.
ESEC-FSE-2019-Cetin #developer #graph #identification #traceability #using
Identifying the most valuable developers using artifact traceability graphs (HAC), pp. 1196–1198.
ESEC-FSE-2019-Ren #automation #migration
Automated patch porting across forked projects (LR), pp. 1199–1201.
ESEC-FSE-2019-Mitropoulos #debugging #evolution #program analysis
Employing different program analysis methods to study bug evolution (CM), pp. 1202–1204.
ESEC-FSE-2019-Tan #kernel #linux #maintenance #multi
Reducing the workload of the Linux kernel maintainers: multiple-committer model (XT), pp. 1205–1207.
ESEC-FSE-2019-Loukeris #performance
Efficient computing in a safe environment (ML), pp. 1208–1210.
ESEC-FSE-2019-Nurgalieva #development #process
The lessons software engineers can extract from painters to improve the software development process (MN), pp. 1211–1213.
ESEC-FSE-2019-Correia #industrial #testing #using
An industrial application of test selection using test suite diagnosability (DC), pp. 1214–1216.
ESEC-FSE-2019-He #comprehension #scalability #source code
Understanding source code comments at large-scale (HH), pp. 1217–1219.
ESEC-FSE-2019-Vandenbogaerde #contract #design #framework #graph
A graph-based framework for analysing the design of smart contracts (BV), pp. 1220–1222.
ESEC-FSE-2019-Valdes
Finding the shortest path to reproduce a failure found by TESTAR (ORV), pp. 1223–1225.
ESEC-FSE-2019-Golzadeh #congruence #dependence #network
Analysing socio-technical congruence in the package dependency network of Cargo (MG), pp. 1226–1228.
ESEC-FSE-2019-He19a #comprehension #debugging #detection #fault #performance
Tuning backfired? not (always) your fault: understanding and detecting configuration-related performance bugs (HH), pp. 1229–1231.
ESEC-FSE-2019-SangleM #on the #open source #python #using
On the use of lambda expressions in 760 open source Python projects (SS, SM), pp. 1232–1234.
ESEC-FSE-2019-Pecorelli #empirical #fault
Test-related factors and post-release defects: an empirical study (FP), pp. 1235–1237.
ESEC-FSE-2019-Pan #analysis #network #robust
Static deep neural network analysis for robustness (RP), pp. 1238–1240.
ESEC-FSE-2019-Khanve #empirical #game studies #smell #web
Are existing code smells relevant in web games? an empirical study (VK), pp. 1241–1243.
ESEC-FSE-2019-Kruger #evolution
Tackling knowledge needs during software evolution (JK), pp. 1244–1246.
ESEC-FSE-2019-Fu #analysis #distributed #on the #scalability
On the scalable dynamic taint analysis for distributed systems (XF), pp. 1247–1249.
ESEC-FSE-2019-Sulun #graph #traceability #using
Suggesting reviewers of software artifacts using traceability graphs (ES), pp. 1250–1252.
ESEC-FSE-2019-Radavelli #modelling #testing #using
Using software testing to repair models (MR), pp. 1253–1255.
ESEC-FSE-2019-Davis #regular expression
Rethinking Regex engines to address ReDoS (JCD), pp. 1256–1258.
ESEC-FSE-2019-Sun #adaptation #testing
Context-aware test case adaptation (PS), pp. 1259–1261.
ESEC-FSE-2019-Gizzatullina #agile #communication #empirical #problem #requirements
Empirical study of customer communication problem in agile requirements engineering (IG), pp. 1262–1264.

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.