ISSTA 2020 55 papers accepted. Updated on 2023-09-08. You can find the lastest information here. WEIZZ: automatic grey-box fuzzing for structured binary formats. Active fuzzing for testing and securing cyber-physical systems. Learning input tokens for effective fuzzing. Fast bit-vector satisfiability. Relocatable addressing model for symbolic execution. Running symbolic execution forever. Can automated program repair refine fault localization? a unified debugging approach. Automated repair of feature interaction failures in automated driving systems. CoCoNuT: combining context-aware neural translation models using ensemble for program repair. Detecting and diagnosing energy issues for mobile applications. Automated classification of actions in bug reports of mobile apps. Data loss detector: automatically revealing data loss bugs in Android apps. Reinforcement learning based curiosity-driven testing of Android applications. Effective white-box testing of deep neural networks with adaptive neuron-selection strategy. DeepGini: prioritizing massive tests to enhance the robustness of deep neural networks. Detecting and understanding real-world differential performance bugs in machine learning libraries. Higher income, larger loan? monotonicity testing of machine learning models. Detecting flaky tests in probabilistic and machine learning applications. Scaffle: bug localization on millions of files. Abstracting failure-inducing inputs. Debugging the performance of Maven's test isolation: experience report. Feedback-driven side-channel analysis for networked applications. Scalable analysis of interaction threats in IoT systems. DeepSQLi: deep semantic learning for testing SQL injection. Dependent-test-aware regression testing techniques. Differential regression testing for REST APIs. Empirically revisiting and enhancing IR-based test-case prioritization. Intermittently failing tests in the embedded systems domain. Feasible and stressful trajectory generation for mobile robots. Detecting cache-related bugs in Spark applications. Patch based vulnerability matching for binary programs. Identifying Java calls in native code via binary scanning. An empirical study on ARM disassembly tools. How effective are smart contract analysis tools? evaluating smart contract static analysis tools using bug injection. A programming model for semi-implicit parallelization of static analyses. Recovering fitness gradients for interprocedural Boolean flags in search-based testing. Scalable build service system with smart scheduling service. Escaping dependency hell: finding build dependency errors with the unified dependency graph. How far we have come: testing decompilation correctness of C decompilers. Discovering discrepancies in numerical libraries. Testing high performance numerical simulation programs: experience, lessons learned, and open issues. Functional code clone detection with syntax and semantics fusion learning. Learning to detect table clones in spreadsheets. ObjSim: lightweight automatic patch prioritization via object similarity. Crowdsourced requirements generation for automatic testing via knowledge graph. TauJud: test augmentation of machine learning in judicial documents. EShield: protect smart contracts against reverse engineering. Echidna: effective, usable, and fast fuzzing for smart contracts. ProFL: a fault localization framework for Prolog. FineLock: automatically refactoring coarse-grained locks into fine-grained locks. CPSDebug: a tool for explanation of failures in cyber-physical systems. Test recommendation system based on slicing coverage filtering. Automated mobile apps testing from visual perspective. Program-aware fuzzing for MQTT applications. Automatic support for the identification of infeasible testing requirements.