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.

@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, GÇ, 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.