Eric Bodden, Wilhelm Schäfer, Arie van Deursen, Andrea Zisman
Proceedings of the 11th Joint Meeting of the 16th European Software Engineering Conference and the 25th Symposium on the Foundations of Software Engineering
ESEC/FSE, 2017.
@proceedings{ESEC-FSE-2017,
editor = "Eric Bodden and Wilhelm Schäfer and Arie van Deursen and Andrea Zisman",
isbn = "978-1-4503-5105-8",
publisher = "{ACM}",
title = "{Proceedings of the 11th Joint Meeting of the 16th European Software Engineering Conference and the 25th Symposium on the Foundations of Software Engineering}",
year = 2017,
}
Contents (124 items)
- ESEC-FSE-2017-Williams #security
- The rising tide lifts all boats: the advancement of science in cyber security (invited talk) (LW), p. 1.
- ESEC-FSE-2017-Easterbrook #how #modelling #verification
- Verifying the forecast: how climate models are developed and tested (invited talk) (SE), p. 2.
- ESEC-FSE-2017-Emmerich #industrial #re-engineering #research
- Software engineering research results in industrial practice: a tale of two projects (invited talk) (WE), p. 3.
- ESEC-FSE-2017-FieldingTEGWKO #architecture #design #rest #web
- Reflections on the REST architectural style and “principled design of the modern web architecture” (impact paper award) (RTF, RNT, JRE, MMG, JW, RK, PO), pp. 4–14.
- ESEC-FSE-2017-YogaN #parallel #performance #profiling #source code
- A fast causal profiler for task parallel programs (AY, SN), pp. 15–26.
- ESEC-FSE-2017-ZhouCMW #kernel #linux #maintenance #on the #scalability
- On the scalability of Linux kernel maintainers' work (MZ, QC, AM, FW), pp. 27–37.
- ESEC-FSE-2017-TsigkanosKG #cyber-physical #evolution #modelling #verification
- Modeling and verification of evolving cyber-physical spaces (CT, TK, CG), pp. 38–48.
- ESEC-FSE-2017-FuM #case study #learning
- Easy over hard: a case study on deep learning (WF0, TM), pp. 49–60.
- ESEC-FSE-2017-OhBMS #product line #random
- Finding near-optimal configurations in product lines by random sampling (JO, DSB, MM, NS), pp. 61–71.
- ESEC-FSE-2017-FuM17a #fault #learning #predict
- Revisiting unsupervised learning for defect prediction (WF0, TM), pp. 72–83.
- ESEC-FSE-2017-XieCZLLL #analysis #named #termination
- Loopster: static loop termination analysis (XX, BC0, LZ, SWL, YL0, XL), pp. 84–94.
- ESEC-FSE-2017-Sidiroglou-Douskos
- CodeCarbonCopy (SSD, EL, AE, FL, MR), pp. 95–105.
- ESEC-FSE-2017-NelsonDDK #power of #why
- The power of “why” and “why not”: enriching scenario exploration with provenance (TN, ND, DJD, SK), pp. 106–116.
- ESEC-FSE-2017-BohmeS0UZ #debugging #empirical #how
- Where is the bug and how is it fixed? an experiment with practitioners (MB, EOS, SC0, EU, AZ), pp. 117–128.
- ESEC-FSE-2017-GopsteinIYDZYC #comprehension #source code
- Understanding misunderstandings in source code (DG, JI, YY, LD, YZ, MKCY, JC), pp. 129–139.
- ESEC-FSE-2017-SiegmundPPAHKBB #comprehension #performance
- Measuring neural efficiency of program comprehension (JS, NP, CP, SA, JH, CK, AB, AB, AB), pp. 140–150.
- ESEC-FSE-2017-MuraliCJ #api #fault #learning #specification
- Bayesian specification learning for finding API usage errors (VM, SC, CJ), pp. 151–162.
- ESEC-FSE-2017-VermaR
- Synergistic debug-repair of heap manipulations (SV, SR), pp. 163–173.
- ESEC-FSE-2017-FerlesWCD
- Failure-directed program trimming (KF, VW, MC, ID), pp. 174–185.
- ESEC-FSE-2017-CoelhoV #open source #why
- Why modern open source projects fail (JC, MTV), pp. 186–196.
- ESEC-FSE-2017-Hilton0TMD #assurance #flexibility #integration #security #trade-off
- Trade-offs in continuous integration: assurance, security, and flexibility (MH, NN0, TT, DM, DD), pp. 197–207.
- ESEC-FSE-2017-JabbarvandM #android #energy #framework #mutation testing #named #testing
- µDroid: an energy-aware mutation testing framework for Android (RJ, SM), pp. 208–219.
- ESEC-FSE-2017-SadeghiJM #android #named #testing #user interface
- PATDroid: permission-aware GUI testing of Android (AS, RJ, SM), pp. 220–232.
- ESEC-FSE-2017-VasquezBTMPVBP #android #mutation testing #testing
- Enabling mutation testing for Android apps (MLV, GB, MT, KM, MDP, CV, CBC, DP), pp. 233–244.
- ESEC-FSE-2017-SuMCWYYPLS #android #modelling #probability #testing #user interface
- Guided, stochastic model-based GUI testing of Android apps (TS, GM, YC, KW, WY, YY, GP, YL0, ZS), pp. 245–256.
- ESEC-FSE-2017-NairMSA #using
- Using bad learners to find good configurations (VN, TM, NS, SA), pp. 257–267.
- ESEC-FSE-2017-SiegmundSA #modelling #variability
- Attributed variability models: outside the comfort zone (NS, SS, SA), pp. 268–278.
- ESEC-FSE-2017-Gazzillo #named
- Kmax: finding all configurations of Kbuild makefiles statically (PG), pp. 279–290.
- ESEC-FSE-2017-KnuppelTMMS #feature model #modelling #product line #question #research
- Is there a mismatch between real-world feature models and product-line research? (AK, TT, SM, JM, IS), pp. 291–302.
- ESEC-FSE-2017-CaiCZ #adaptation #generative #quality
- Adaptively generating high quality fixes for atomicity violations (YC0, LC, JZ), pp. 303–314.
- ESEC-FSE-2017-GuoCY #concurrent #detection #execution #named #thread
- AtexRace: across thread and execution sampling for in-house race detection (YG, YC0, ZY), pp. 315–325.
- ESEC-FSE-2017-GuoWW #execution #logic #programmable #symbolic computation
- Symbolic execution of programmable logic controller code (SG, MW, CW0), pp. 326–336.
- ESEC-FSE-2017-KusanoW #memory management #modelling #static analysis #thread
- Thread-modular static analysis for relaxed memory models (MK, CW0), pp. 337–348.
- ESEC-FSE-2017-AliabadiKGP #cyber-physical #detection #invariant #named #security
- ARTINALI: dynamic invariant detection for cyber-physical system security (MRA, AAK, JGS, KP), pp. 349–361.
- ESEC-FSE-2017-KuventMR #specification
- A symbolic justice violations transition system for unrealizable GR(1) specifications (AK, SM, JOR), pp. 362–372.
- ESEC-FSE-2017-MaggioPFH #automation #multi #using
- Automated control of multiple software goals using multiple actuators (MM, AVP, AF, HH), pp. 373–384.
- ESEC-FSE-2017-AbdalkareemNWMS #case study #developer #empirical #why
- Why do developers use trivial packages? an empirical case study on npm (RA, ON, SW, SM, ES), pp. 385–395.
- ESEC-FSE-2017-ChaparroLZMPMBN #debugging #detection
- Detecting missing information in bug descriptions (OC, JL, FZ, LM, MDP, AM, GB, VN), pp. 396–407.
- ESEC-FSE-2017-ZibaeenejadZA #feature model #interactive
- Continuous variable-specific resolutions of feature interactions (MHZ, CZ, JMA), pp. 408–418.
- ESEC-FSE-2017-BagherzadehHD #debugging #development #independence #modelling #platform #realtime
- Model-level, platform-independent debugging in the context of the model-driven development of real-time systems (MB, NH, JD), pp. 419–430.
- ESEC-FSE-2017-SorensenED #algorithm #gpu #kernel #multi
- Cooperative kernels: GPU multitasking for blocking algorithms (TS0, HE, AFD), pp. 431–441.
- ESEC-FSE-2017-GarbervetskyZL #distributed #static analysis #towards
- Toward full elasticity in distributed static analysis: the case of callgraph analysis (DG, EZ, BL), pp. 442–453.
- ESEC-FSE-2017-LlerenaSR #in the cloud #model checking #probability
- Probabilistic model checking of perturbed MDPs with applications to cloud computing (YRSL, GS, DSR), pp. 454–464.
- ESEC-FSE-2017-CedrimGMGSMFRC #comprehension #refactoring #smell
- Understanding the impact of refactoring on smells: a longitudinal study of 23 software projects (DC, AG, MM, RG, LdSS, RMdM, BF, MR, AC), pp. 465–475.
- ESEC-FSE-2017-RastogiDCJM #automation #named
- Cimplifier: automatically debloating containers (VR, DD, LDC, SJ, PDM), pp. 476–486.
- ESEC-FSE-2017-DietschHMNP #model checking
- Craig vs. Newton in software model checking (DD, MH, BM, AN, AP), pp. 487–497.
- ESEC-FSE-2017-GalhotraBM #testing
- Fairness testing: testing software for discrimination (SG, YB, AM), pp. 498–510.
- ESEC-FSE-2017-BrownVLR
- The care and feeding of wild-caught mutants (DBB, MV, BL, TWR), pp. 511–522.
- ESEC-FSE-2017-WangNT #named #testing
- QTEP: quality-aware test case prioritization (SW0, JN, LT0), pp. 523–534.
- ESEC-FSE-2017-BrennanTRAB #constraints #normalisation #program analysis
- Constraint normalization and parameterized caching for quantitative program analysis (TB, NT, NR, AA, TB), pp. 535–546.
- ESEC-FSE-2017-GoldBHIKY #modelling #slicing
- Generalized observational slicing for tree-represented modelling languages (NEG, DWB, MH, SSI, JK, SY), pp. 547–558.
- ESEC-FSE-2017-AlrajehPN #forensics #on the #requirements
- On evidence preservation requirements for forensic-ready systems (DA, LP, BN), pp. 559–569.
- ESEC-FSE-2017-PastoreMM #behaviour #identification #named
- BDCI: behavioral driven conflict identification (FP, LM, DM), pp. 570–581.
- ESEC-FSE-2017-DAntoniSV #named
- NoFAQ: synthesizing command repairs from examples (LD, RS, MV), pp. 582–592.
- ESEC-FSE-2017-LeCLGV #named #programming #semantics #synthesis
- S3: syntax- and semantic-guided repair synthesis via programming by examples (XBDL, DHC, DL0, CLG, WV), pp. 593–604.
- ESEC-FSE-2017-NguyenARH #approach #invariant
- Counterexample-guided approach to finding numerical invariants (TN, TA, AR, MH0), pp. 605–615.
- ESEC-FSE-2017-SmithFA #relational #specification
- Discovering relational specifications (CS, GF, AA), pp. 616–626.
- ESEC-FSE-2017-LiCCLLT #fuzzing #named
- Steelix: program-state based binary fuzzing (YL, BC0, MC, SWL, YL0, AT), pp. 627–637.
- ESEC-FSE-2017-GlanzAERHLM #named #obfuscation
- CodeMatch: obfuscation won't conceal your repackaged app (LG, SA, ME, MR, BH, JL, MM), pp. 638–648.
- ESEC-FSE-2017-0001RS #compilation #verification
- A compiler and verifier for page access oblivious computation (RS0, SKR, SAS), pp. 649–660.
- ESEC-FSE-2017-GarciaHGM #android #automation #communication #component #generative
- Automatic generation of inter-component communication exploits for Android applications (JG, MH, NG, SM), pp. 661–671.
- ESEC-FSE-2017-WeiLC #android #named #static analysis
- OASIS: prioritizing static analysis warnings for Android apps based on app user reviews (LW, YL, SCC), pp. 672–682.
- ESEC-FSE-2017-VasilescuCD #identifier #obfuscation
- Recovering clear, natural identifiers from obfuscated JS names (BV, CC, PTD), pp. 683–693.
- ESEC-FSE-2017-YuZW #concurrent #named
- DESCRY: reproducing system-level concurrency failures (TY, TSZ, CW0), pp. 694–704.
- ESEC-FSE-2017-BianchiPT #concurrent
- Reproducing concurrency failures from crash stacks (FAB, MP, VT), pp. 705–716.
- ESEC-FSE-2017-CastelluccioSVP #automation #correlation
- Automatically analyzing groups of crashes for finding correlations (MC, CS, LV, GP), pp. 717–726.
- ESEC-FSE-2017-LongAR #automation #generative
- Automatic inference of code transforms for patch generation (FL, PA, MR), pp. 727–739.
- ESEC-FSE-2017-YiAKTR #automation #program repair #programming #using
- A feasibility study of using automated program repair for introductory programming assignments (JY, UZA, AK, SHT, AR), pp. 740–751.
- ESEC-FSE-2017-TianR #automation #c #debugging #fault
- Automatically diagnosing and repairing error handling bugs in C (YT, BR), pp. 752–762.
- ESEC-FSE-2017-HellendoornD #modelling #network #question #source code
- Are deep neural networks the best choice for modeling source code? (VJH, PTD), pp. 763–773.
- ESEC-FSE-2017-MartieHK #code search #comprehension
- Understanding the impact of support for iteration on code search (LM, AvdH, TK), pp. 774–785.
- ESEC-FSE-2017-MaAXLZLZ #algorithm #graph #machine learning #named
- LAMP: data provenance for graph based machine learning algorithms through derivative computation (SM, YA, ZX, WCL, JZ, YL, XZ0), pp. 786–797.
- ESEC-FSE-2017-DotzlerKKP #recommendation
- More accurate recommendations for method-level changes (GD, MK, PK, MP), pp. 798–808.
- ESEC-FSE-2017-CelikVMG #bound #testing #virtual machine
- Regression test selection across JVM boundaries (AÇ, MV, AM, MG), pp. 809–820.
- ESEC-FSE-2017-LabuschagneIH #case study #cost analysis #integration #java #testing #using
- Measuring the cost of regression testing in practice: a study of Java projects using continuous integration (AL, LI, RH), pp. 821–830.
- ESEC-FSE-2017-YangZLT #automation #program repair #testing
- Better test cases for better automated program repair (JY0, AZ, YL, LT0), pp. 831–841.
- ESEC-FSE-2017-BuXXZTX #android #industrial #internet #mobile #program analysis #security
- When program analysis meets mobile security: an industrial study of misusing Android internet sockets (WB, MX, LX, YZ, ZT, TX), pp. 842–847.
- ESEC-FSE-2017-VasicPMG #dot-net #testing
- File-level vs. module-level regression test selection for .NET (MV, ZP, AM, MG), pp. 848–853.
- ESEC-FSE-2017-LamWLWZLYDX #android #case study #industrial #question
- Record and replay for Android: are we there yet in industrial cases? (WL, ZW, DL0, WW, HZ, HL, PY, YD, TX0), pp. 854–859.
- ESEC-FSE-2017-DijkCHB #modelling #network #privacy #re-engineering
- Model-driven software engineering in practice: privacy-enhanced filtering of network traffic (RvD, CC, JvdH, JvdB), pp. 860–865.
- ESEC-FSE-2017-HuijgensLSRGR #agile #delivery #metric #mining #power of #predict
- Strong agile metrics: mining log data to determine predictive power of software metrics for continuous delivery teams (HH, RL, DS, HR, GG, DR), pp. 866–871.
- ESEC-FSE-2017-VolfS #heuristic
- Screening heuristics for project gating systems (ZV, ES), pp. 872–877.
- ESEC-FSE-2017-SahaGMM #framework #natural language #platform #query
- Natural language querying in SAP-ERP platform (DS, NG, SM, BM), pp. 878–883.
- ESEC-FSE-2017-AdzicC #architecture
- Serverless computing: economic and architectural impact (GA, RC), pp. 884–889.
- ESEC-FSE-2017-IvanovRSYZ #research #what
- What do software engineers care about? gaps between research and practice (VI0, AR, GS, JY, VZ), pp. 890–895.
- ESEC-FSE-2017-GalsterAMT #agile #architecture #question
- Reference architectures and Scrum: friends or foes? (MG, SA, SMF, DT), pp. 896–901.
- ESEC-FSE-2017-HarmsRI #architecture #guidelines
- Guidelines for adopting frontend architectures and patterns in microservices-based systems (HH, CR, LLI), pp. 902–907.
- ESEC-FSE-2017-GarciaG #agile #comprehension
- Improving understanding of dynamically typed software developed by agile practitioners (JG, KG), pp. 908–913.
- ESEC-FSE-2017-ZhouS #automation #commit #debugging #identification #security
- Automated identification of security issues from commit messages and bug reports (YZ, AS), pp. 914–919.
- ESEC-FSE-2017-WuY #android #framework #kernel #named
- LaChouTi: kernel vulnerability responding framework for the fragmented Android devices (JW, MY), pp. 920–925.
- ESEC-FSE-2017-LeeHLKJ #automation #debugging #industrial #learning
- Applying deep learning based automatic bug triager to industrial projects (SRL, MJH, CGL, MK, GJ), pp. 926–931.
- ESEC-FSE-2017-GarbervetskyP0M #big data #optimisation #query #static analysis
- Static analysis for optimizing big data queries (DG, ZP, MB0, MM, TM, EZ), pp. 932–937.
- ESEC-FSE-2017-MatinnejadNB #automation #case study #hybrid #industrial #testing
- Automated testing of hybrid Simulink/Stateflow controllers: industrial case studies (RM, SN, LCB), pp. 938–943.
- ESEC-FSE-2017-DovgalyukFVM #framework #virtual machine
- QEMU-based framework for non-intrusive virtual machine instrumentation and introspection (PD, NF, IV, VM), pp. 944–948.
- ESEC-FSE-2017-YuanXXPZ #android #execution #graph #named
- RunDroid: recovering execution call graphs for Android applications (YY, LX, XX, AP, HZ), pp. 949–953.
- ESEC-FSE-2017-YuCZWD #java #named
- RGSE: a regular property guided symbolic executor for Java (HY, ZC, YZ, JW0, WD0), pp. 954–958.
- ESEC-FSE-2017-ErataGTK #automation #configuration management #reasoning #semantics
- A tool for automated reasoning about traces based on configurable formal semantics (FE, AG, BT, GK), pp. 959–963.
- ESEC-FSE-2017-PastoreM #automation #detection #fault #named
- VART: a tool for the automatic detection of regression faults (FP, LM), pp. 964–968.
- ESEC-FSE-2017-RegisCBPRPAGF #alloy #analysis #behaviour #modelling #specification
- DynAlloy analyzer: a tool for the specification and analysis of alloy models with dynamic behaviour (GR, CC, SGB, MP, FR, PP, NA, JPG, MFF), pp. 969–973.
- ESEC-FSE-2017-GreenyerGKDSW #modelling #programming
- From scenario modeling to scenario programming for reactive systems with dynamic topology (JG, DG, FK, JD, JS, EW), pp. 974–978.
- ESEC-FSE-2017-RegisDDA #lts #named
- CLTSA: labelled transition system analyser with counting fluent support (GR, RD, ND, NA), pp. 979–983.
- ESEC-FSE-2017-DebreceniBBRV #collaboration #framework #modelling #version control
- The MONDO collaboration framework: secure collaborative modeling over existing version control systems (CD, GB, MB, IR, DV), pp. 984–988.
- ESEC-FSE-2017-AhmadianPRJ #analysis #modelling #privacy #security
- Model-based privacy and security analysis with CARiSMA (ASA, SP, QR, JJ), pp. 989–993.
- ESEC-FSE-2017-BunyakiatiP #commit #multi
- Cherry-picking of code commits in long-running, multi-release software (PB, CP), pp. 994–998.
- ESEC-FSE-2017-NunezMR #comprehension #named
- ARCC: assistant for repetitive code comprehension (WZN, VJM, CRR), pp. 999–1003.
- ESEC-FSE-2017-ThomeSBB #injection #named
- JoanAudit: a tool for auditing common injection vulnerabilities (JT, LKS, DB, LCB), pp. 1004–1008.
- ESEC-FSE-2017-XuXXLL #named #retrieval
- XSearch: a domain-specific cross-language relevant question retrieval tool (BX, ZX, XX0, DL0, XBDL), pp. 1009–1013.
- ESEC-FSE-2017-Wang #adaptation #nondeterminism #re-engineering #search-based #self #using
- Using search-based software engineering to handle the changes with uncertainties for self-adaptive systems (LW), pp. 1014–1017.
- ESEC-FSE-2017-Oliveira #architecture #co-evolution #dependence #fine-grained #named #refactoring #using
- DRACO: discovering refactorings that improve architecture using fine-grained co-change dependencies (MCdO0), pp. 1018–1021.
- ESEC-FSE-2017-Abusair #development #mobile
- User- and analysis-driven context aware software development in mobile computing (MA), pp. 1022–1025.
- ESEC-FSE-2017-Kogel #development #modelling #recommendation
- Recommender system for model driven software development (SK), pp. 1026–1029.
- ESEC-FSE-2017-Ellmann #development #documentation #on the #similarity
- On the similarity of software development documentation (ME), pp. 1030–1033.
- ESEC-FSE-2017-Schuler #development #evolution #mobile #optimisation #re-engineering #search-based #testing
- Application of search-based software engineering methodologies for test suite optimization and evolution in mission critical mobile application development (AS), pp. 1034–1037.
- ESEC-FSE-2017-Kafer #communication #re-engineering
- Summarizing software engineering communication artifacts from different sources (VK), pp. 1038–1041.
- ESEC-FSE-2017-Nigar #modelling #scheduling
- Model-based dynamic software project scheduling (NN), pp. 1042–1045.
- ESEC-FSE-2017-Tang #design #optimisation #performance
- System performance optimization via design and configuration space exploration (CT0), pp. 1046–1049.
- ESEC-FSE-2017-Jaffe #approach
- Suggesting meaningful variable names for decompiled code: a machine translation approach (AJ), pp. 1050–1052.
- ESEC-FSE-2017-Yu #verification
- Practical symbolic verification of regular properties (HY), pp. 1053–1055.
- ESEC-FSE-2017-Pashchenko #benchmark #difference #metric #security #static analysis #testing #tool support
- FOSS version differentiation as a benchmark for static analysis security testing tools (IP), pp. 1056–1058.
- ESEC-FSE-2017-Kohli #android #identification #named
- DecisionDroid: a supervised learning-based system to identify cloned Android applications (AK), pp. 1059–1061.
- ESEC-FSE-2017-Abdalkareem #developer #perspective #using
- Reasons and drawbacks of using trivial npm packages: the developers' perspective (RA), pp. 1062–1064.
- ESEC-FSE-2017-Mujahid #android #case study #detection #smarttech
- Detecting wearable app permission mismatches: a case study on Android wear (SM), pp. 1065–1067.
- ESEC-FSE-2017-Mills #automation #classification #traceability
- Automating traceability link recovery through classification (CM), pp. 1068–1070.
- ESEC-FSE-2017-Schramm #automation #heuristic #performance #program repair #using
- Improving performance of automatic program repair using learned heuristics (LS), pp. 1071–1073.