PreciseBugCollector: Extensible, Executable and Precise Bug-fix Collection

Bug datasets are vital for enabling deep learning techniques to address software maintenance tasks related to bugs. However, existing bug datasets suffer from precise and scale limitations: they are either small-scale but precise with manual validation or large-scale but imprecise with simple commit message processing. In this paper, we introduce PreciseBugCollector, a precise, multi-language bug collection approach that overcomes these two limitations. PreciseBugCollector is based on two novel components: a) A bug tracker to map the codebase repositories with external bug repositories to trace bug type information, and b) A bug injector to generate project-specific bugs by injecting noise into the correct codebases and then executing them against their test suites to obtain test failure messages. We implement PreciseBugCollector against three sources: 1) A bug tracker that links to the national vulnerability data set (NVD) to collect general-wise vulnerabilities, 2) A bug tracker that links to OSS-Fuzz to collect general-wise bugs, and 3) A bug injector based on 16 injection rules to generate project-wise bugs. To date, PreciseBugCollector comprises 1057818 bugs extracted from 2968 open-source projects. Of these, 12602 bugs are sourced from bug repositories (NVD and OSS-Fuzz), while the remaining 1045216 project-specific bugs are generated by the bug injector. Considering the challenge objectives, we argue that a bug injection approach is highly valuable for the industrial setting, since project-specific bugs align with domain knowledge, share the same codebase, and adhere to the coding style employed in industrial projects.Comment: Accepted at the industry challenge track of ASE 202

Empirical Study of Restarted and Flaky Builds on Travis CI

Continuous Integration (CI) is a development practice where developers frequently integrate code into a common codebase. After the code is integrated, the CI server runs a test suite and other tools to produce a set of reports (e.g., output of linters and tests). If the result of a CI test run is unexpected, developers have the option to manually restart the build, re-running the same test suite on the same code; this can reveal build flakiness, if the restarted build outcome differs from the original build. In this study, we analyze restarted builds, flaky builds, and their impact on the development workflow. We observe that developers restart at least 1.72% of builds, amounting to 56,522 restarted builds in our Travis CI dataset. We observe that more mature and more complex projects are more likely to include restarted builds. The restarted builds are mostly builds that are initially failing due to a test, network problem, or a Travis CI limitations such as execution timeout. Finally, we observe that restarted builds have a major impact on development workflow. Indeed, in 54.42% of the restarted builds, the developers analyze and restart a build within an hour of the initial failure. This suggests that developers wait for CI results, interrupting their workflow to address the issue. Restarted builds also slow down the merging of pull requests by a factor of three, bringing median merging time from 16h to 48h

Contextual Predictive Mutation Testing

Mutation testing is a powerful technique for assessing and improving test suite quality that artificially introduces bugs and checks whether the test suites catch them. However, it is also computationally expensive and thus does not scale to large systems and projects. One promising recent approach to tackling this scalability problem uses machine learning to predict whether the tests will detect the synthetic bugs, without actually running those tests. However, existing predictive mutation testing approaches still misclassify 33% of detection outcomes on a randomly sampled set of mutant-test suite pairs. We introduce MutationBERT, an approach for predictive mutation testing that simultaneously encodes the source method mutation and test method, capturing key context in the input representation. Thanks to its higher precision, MutationBERT saves 33% of the time spent by a prior approach on checking/verifying live mutants. MutationBERT, also outperforms the state-of-the-art in both same project and cross project settings, with meaningful improvements in precision, recall, and F1 score. We validate our input representation, and aggregation approaches for lifting predictions from the test matrix level to the test suite level, finding similar improvements in performance. MutationBERT not only enhances the state-of-the-art in predictive mutation testing, but also presents practical benefits for real-world applications, both in saving developer time and finding hard to detect mutants

Mind the Gap: The Difference Between Coverage and Mutation Score Can Guide Testing Efforts

An "adequate" test suite should effectively find all inconsistencies between a system's requirements/specifications and its implementation. Practitioners frequently use code coverage to approximate adequacy, while academics argue that mutation score may better approximate true (oracular) adequacy coverage. High code coverage is increasingly attainable even on large systems via automatic test generation, including fuzzing. In light of all of these options for measuring and improving testing effort, how should a QA engineer spend their time? We propose a new framework for reasoning about the extent, limits, and nature of a given testing effort based on an idea we call the oracle gap, or the difference between source code coverage and mutation score for a given software element. We conduct (1) a large-scale observational study of the oracle gap across popular Maven projects, (2) a study that varies testing and oracle quality across several of those projects and (3) a small-scale observational study of highly critical, well-tested code across comparable blockchain projects. We show that the oracle gap surfaces important information about the extent and quality of a test effort beyond either adequacy metric alone. In particular, it provides a way for practitioners to identify source files where it is likely a weak oracle tests important code