| Quality of Automated Program Repair on Real-World Defects | 2 | 0.36 | 2022 |
| Hypothesis Formalization: Empirical Findings, Software Limitations, and Design Implications | 0 | 0.34 | 2022 |
| Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships | 0 | 0.34 | 2022 |
| Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition | 0 | 0.34 | 2022 |
| Prioritizing Mutants to Guide Mutation Testing | 0 | 0.34 | 2022 |
| Does mutation testing improve testing practices? | 1 | 0.35 | 2021 |
| Revisiting the Relationship Between Fault Detection, Test Adequacy Criteria, and Test Set Size | 4 | 0.39 | 2020 |
| Defects4J as a Challenge Case for the Search-Based Software Engineering Community. | 1 | 0.36 | 2020 |
| Tea: A High-level Language and Runtime System for Automating Statistical Analysis. | 1 | 0.35 | 2019 |
| Guiding testing effort using mutant utility | 0 | 0.34 | 2019 |
| Code coverage at Google. | 3 | 0.37 | 2019 |
| Medusa: Mutant Equivalence Detection Using Satisfiability Analysis | 2 | 0.37 | 2019 |
| Special issue on mutation testing and analysis. | 1 | 0.36 | 2019 |
| Comparing developer-provided to user-provided tests for fault localization and automated program repair. | 5 | 0.38 | 2018 |
| Do automated program repair techniques repair hard and important bugs? | 5 | 0.37 | 2018 |
| An Industrial Application of Mutation Testing: Lessons, Challenges, and Research Directions | 7 | 0.42 | 2018 |
| Special issue on Mutation Testing. | 1 | 0.36 | 2017 |
| Inferring mutant utility from program context. | 9 | 0.46 | 2017 |
| Private API Access and Functional Mocking in Automated Unit Test Generation | 5 | 0.43 | 2017 |
| Evaluating and improving fault localization. | 57 | 1.09 | 2017 |
| Tailored Mutants Fit Bugs Better. | 1 | 0.35 | 2016 |
| Unit testing tool competition: round four. | 3 | 0.40 | 2016 |
| Static Analysis of Implicit Control Flow: Resolving Java Reflection and Android Intents (T) | 26 | 0.74 | 2015 |
| Collaborative Verification of Information Flow for a High-Assurance App Store. | 0 | 0.34 | 2015 |
| Mutation Analysis for the Real World: Effectiveness, Efficiency, and Proper Tool Support. | 0 | 0.34 | 2015 |
| Higher accuracy and lower run time: efficient mutation analysis using non-redundant mutation operators | 19 | 0.57 | 2015 |
| Do Automatically Generated Unit Tests Find Real Faults? An Empirical Study of Effectiveness and Challenges (T) | 53 | 1.06 | 2015 |
| Are mutants a valid substitute for real faults in software testing? | 211 | 4.43 | 2014 |
| Defects4J: a database of existing faults to enable controlled testing studies for Java programs | 246 | 5.43 | 2014 |
| Collaborative Verification of Information Flow for a High-Assurance App Store | 37 | 0.98 | 2014 |
| The major mutation framework: efficient and scalable mutation analysis for Java | 57 | 2.14 | 2014 |
| Efficient mutation analysis by propagating and partitioning infected execution states | 35 | 1.13 | 2014 |
| Using State Infection Conditions to Detect Equivalent Mutants and Speed up Mutation Analysis | 11 | 0.56 | 2013 |
| Do Redundant Mutants Affect the Effectiveness and Efficiency of Mutation Analysis? | 32 | 1.07 | 2012 |
| Using Non-redundant Mutation Operators and Test Suite Prioritization to Achieve Efficient and Scalable Mutation Analysis | 31 | 1.51 | 2012 |
| MAJOR: An efficient and extensible tool for mutation analysis in a Java compiler | 53 | 1.70 | 2011 |
| Using conditional mutation to increase the efficiency of mutation analysis | 16 | 0.98 | 2011 |
| Automating unit and integration testing with partial oracles | 5 | 0.41 | 2011 |
| Automating software tests with partial oracles in integrated environments | 3 | 0.42 | 2010 |
| Evaluating Testing Strategies for Imaging Software by Means of Mutation Analysis | 5 | 0.48 | 2009 |
| Benchmarking Testing Strategies with Tools from Mutation Analysis | 10 | 0.60 | 2008 |
