Benchmarks of bugs are essential to empirically evaluate automatic program repair tools. In this paper, we present Bears, a project for collecting and storing bugs into an extensible bug benchmark for automatic repair studies in Java. The collection of bugs relies on commit building state from Continuous Integration (CI) to find potential pairs of buggy and patched program versions from open-source projects hosted on GitHub. Each pair of program versions passes through a pipeline where an attempt of reproducing a bug and its patch is performed. The core step of the reproduction pipeline is the execution of the test suite of the program on both program versions. If a test failure is found in the buggy program version candidate and no test failure is found in its patched program version candidate, a bug and its patch were successfully reproduced. The uniqueness of Bears is the usage of CI (builds) to identify buggy and patched program version candidates, which has been widely adopted in the last years in open-source projects. This approach allows us to collect bugs from a diversity of projects beyond mature projects that use bug tracking systems. Moreover, Bears was designed to be publicly available and to be easily extensible by the research community through automatic creation of branches with bugs in a given GitHub repository, which can be used for pull requests in the Bears repository. We present in this paper the approach employed by Bears, and we deliver the version 1.0 of Bears, which contains 251 reproducible bugs collected from 72 projects that use the Travis CI and Maven build environment.
The closest benchmarks to are Defects4J @cite_12 and Bugs.jar @cite_0 , both for Java. Defects4J contains 395 reproducible bugs collected from six projects, and Bugs.jar contains 1,158 reproducible bugs collected from eight Apache projects. To collect bugs, the approach used for both benchmarks is based on bug tracking systems, and they contain bugs from large, mature projects. , on the other hand, was designed to collect bugs from a diversity of projects other than large and mature ones: we break the need of projects using bug tracking systems. Note that bug tracking systems are used in the direction of documenting bugs. Continuous Integration, on the other hand, is used to actually build and test a project, which is closer to the task of identifying reproducible bugs.
@cite_0 @cite_12
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We present Bugs.jar, a large-scale dataset for research in automated debugging, patching, and testing of Java programs. Bugs.jar is comprised of 1,158 bugs and patches, drawn from 8 large, popular open-source Java projects, spanning 8 diverse and prominent application categories. It is an order of magnitude larger than Defects4J, the only other dataset in its class. We discuss the methodology used for constructing Bugs.jar, the representation of the dataset, several use-cases, and an illustration of three of the use-cases through the application of 3 specific tools on Bugs.jar, namely our own tool, E lixir , and two third-party tools, Ekstazi and JaCoCo. Empirical studies in software testing research may not be comparable, reproducible, or characteristic of practice. One reason is that real bugs are too infrequently used in software testing research. Extracting and reproducing real bugs is challenging and as a result hand-seeded faults or mutants are commonly used as a substitute. This paper presents Defects4J, a database and extensible framework providing real bugs to enable reproducible studies in software testing research. The initial version of Defects4J contains 357 real bugs from 5 real-world open source pro- grams. Each real bug is accompanied by a comprehensive test suite that can expose (demonstrate) that bug. Defects4J is extensible and builds on top of each program’s version con- trol system. Once a program is configured in Defects4J, new bugs can be added to the database with little or no effort. Defects4J features a framework to easily access faulty and fixed program versions and corresponding test suites. This framework also provides a high-level interface to common tasks in software testing research, making it easy to con- duct and reproduce empirical studies. Defects4J is publicly available at http: defects4j.org.