| A Survey on How Test Flakiness Affects Developers and What Support They Need To Address It | 0 | 0.34 | 2022 |
| Common Patterns in Block-Based Robot Programs. | 0 | 0.34 | 2022 |
| Generating Critical Driving Scenarios from Accident Sketches | 0 | 0.34 | 2022 |
| Model-based Testing of Scratch Programs | 0 | 0.34 | 2022 |
| Effects of Hints on Debugging Scratch Programs - An Empirical Study with Primary School Teachers in Training. | 0 | 0.34 | 2021 |
| Improving Readability of Scratch Programs with Search-based Refactoring | 0 | 0.34 | 2021 |
| Graph-based seed object synthesis for search-based unit testing | 3 | 0.38 | 2021 |
| Data-driven Analysis of Gender Differences and Similarities in Scratch Programs. | 0 | 0.34 | 2021 |
| An Empirical Study of Flaky Tests in Python | 4 | 0.39 | 2021 |
| SnapCheck: Automated Testing for Snap! Programs | 1 | 0.35 | 2021 |
| An Empirical Study to Determine if Mutants Can Effectively Simulate Students' Programming Mistakes to Increase Tutors' Confidence in Autograding | 1 | 0.35 | 2021 |
| Encoding the certainty of boolean variables to improve the guidance for search-based test generation | 0 | 0.34 | 2021 |
| Does mutation testing improve testing practices? | 1 | 0.35 | 2021 |
| LitterBox: A Linter for Scratch Programs | 1 | 0.39 | 2021 |
| Measuring and Maintaining Population Diversity in Search-Based Unit Test Generation. | 0 | 0.34 | 2020 |
| Automated Unit Test Generation for Python | 3 | 0.39 | 2020 |
| Revisiting the Relationship Between Fault Detection, Test Adequacy Criteria, and Test Set Size | 4 | 0.39 | 2020 |
| Recovering Fitness Gradients for Interprocedural Boolean Flags in Search-Based Testing | 4 | 0.40 | 2020 |
| Search-based Testing for Scratch Programs | 1 | 0.36 | 2020 |
| EvoSuite at the SBST 2020 Tool Competition | 0 | 0.34 | 2020 |
| Verified from scratch: program analysis for learners' programs | 0 | 0.34 | 2020 |
| Causes and effects of fitness landscapes in unit test generation | 0 | 0.34 | 2020 |
| Automatically reconstructing car crashes from police reports for testing self-driving cars | 1 | 0.37 | 2019 |
| Improving Scratch Programming with CRC-Card Design | 1 | 0.36 | 2019 |
| An Empirical Evaluation of Search Algorithms for App Testing. | 3 | 0.38 | 2019 |
| Testing scratch programs automatically. | 1 | 0.36 | 2019 |
| Gamifying a Software Testing Course with Code Defenders | 2 | 0.51 | 2019 |
| Code coverage at Google. | 3 | 0.37 | 2019 |
| Search-based detection of deviation failures in the migration of legacy spreadsheet applications. | 0 | 0.34 | 2018 |
| A Tutorial on Using and Extending the EvoSuite Search-Based Test Generator. | 1 | 0.35 | 2018 |
| Special issue on automation of software testing: improving practical applicability. | 0 | 0.34 | 2018 |
| How Do Automatically Generated Unit Tests Influence Software Maintenance? | 2 | 0.37 | 2018 |
| Using controlled numbers of real faults and mutants to empirically evaluate coverage-based test case prioritization. | 1 | 0.35 | 2018 |
| Modelling Hand Gestures to Test Leap Motion Controlled Applications | 0 | 0.34 | 2018 |
| Gamification of Software Testing. | 1 | 0.37 | 2017 |
| Code defenders: crowdsourcing effective tests and subtle mutants with a mutation testing game. | 2 | 0.41 | 2017 |
| Private API Access and Functional Mocking in Automated Unit Test Generation | 5 | 0.43 | 2017 |
| An Empirical Evaluation of Evolutionary Algorithms for Test Suite Generation. | 10 | 0.54 | 2017 |
| Disposable testing: avoiding maintenance of generated unit tests by throwing them away. | 0 | 0.34 | 2017 |
| Uncertainty-Driven Black-Box Test Data Generation | 3 | 0.40 | 2017 |
| Code Defenders: A Mutation Testing Game. | 4 | 0.53 | 2016 |
| Seeding strategies in search-based unit test generation. | 20 | 0.66 | 2016 |
| Java Enterprise Edition Support in Search-Based JUnit Test Generation. | 3 | 0.43 | 2016 |
| Automated search for good coverage criteria: moving from code coverage to fault coverage through search-based software engineering. | 4 | 0.40 | 2016 |
| Automated search for good coverage criteria: moving from code coverage to fault coverage through search-based software engineering. | 4 | 0.40 | 2016 |
| Generating Readable Unit Tests for Guava | 5 | 0.39 | 2015 |
| 1600 faults in 100 projects: automatically finding faults while achieving high coverage with EvoSuite | 37 | 1.05 | 2015 |
| Using dynamic symbolic execution to generate inputs in search-based GUI testing | 5 | 0.45 | 2015 |
| Continuous Test Generation on Guava | 0 | 0.34 | 2015 |
| Parameter Control in Search-Based Generation of Unit Test Suites | 2 | 0.40 | 2015 |
