Fabian Trautsch

Zaheed Ahmed, Eike Stein, Steffen Herbold et al.

The competent programmer hypothesis states that most programmers are competent enough to create correct or almost correct source code. Because this implies that bugs should usually manifest through small variations of the correct code, the competent programmer hypothesis is one of the fundamental assumptions of mutation testing. Unfortunately, it is still unclear if the competent programmer hypothesis holds and past research presents contradictory claims. Within this article, we provide a new perspective on the competent programmer hypothesis and its relation to mutation testing. We try to re-create real-world bugs through chains of mutations to understand if there is a direct link between mutation testing and bugs. The lengths of these paths help us to understand if the source code is really almost correct, or if large variations are required. Our results indicate that while the competent programmer hypothesis seems to be true, mutation testing is missing important operators to generate representative real-world bugs.

Alexander Trautsch, Fabian Trautsch, Steffen Herbold

The SmartSHARK repository mining data is a collection of rich and detailed information about the evolution of software projects. The data is unique in its diversity and contains detailed information about each change, issue tracking data, continuous integration data, as well as pull request and code review data. Moreover, the data does not contain only raw data scraped from repositories, but also annotations in form of labels determined through a combination of manual analysis and heuristics, as well as links between the different parts of the data set. The SmartSHARK data set provides a rich source of data that enables us to explore research questions that require data from different sources and/or longitudinal data over time.

Steffen Herbold, Alexander Trautsch, Fabian Trautsch

Context: Issue tracking systems are used to track and describe tasks in the development process, e.g., requested feature improvements or reported bugs. However, past research has shown that the reported issue types often do not match the description of the issue. Objective: We want to understand the overall maturity of the state of the art of issue type prediction with the goal to predict if issues are bugs and evaluate if we can improve existing models by incorporating manually specified knowledge about issues. Method: We train different models for the title and description of the issue to account for the difference in structure between these fields, e.g., the length. Moreover, we manually detect issues whose description contains a null pointer exception, as these are strong indicators that issues are bugs. Results: Our approach performs best overall, but not significantly different from an approach from the literature based on the fastText classifier from Facebook AI Research. The small improvements in prediction performance are due to structural information about the issues we used. We found that using information about the content of issues in form of null pointer exceptions is not useful. We demonstrate the usefulness of issue type prediction through the example of labelling bugfixing commits. Conclusions: Issue type prediction can be a useful tool if the use case allows either for a certain amount of missed bug reports or the prediction of too many issues as bug is acceptable.

Alexander Trautsch, Fabian Trautsch, Steffen Herbold et al.

Software repository mining is the foundation for many empirical software engineering studies. The collection and analysis of detailed data can be challenging, especially if data shall be shared to enable replicable research and open science practices. SmartSHARK is an ecosystem that supports replicable and reproducible research based on software repository mining.

Steffen Herbold, Alexander Trautsch, Fabian Trautsch et al.

Context: The SZZ algorithm is the de facto standard for labeling bug fixing commits and finding inducing changes for defect prediction data. Recent research uncovered potential problems in different parts of the SZZ algorithm. Most defect prediction data sets provide only static code metrics as features, while research indicates that other features are also important. Objective: We provide an empirical analysis of the defect labels created with the SZZ algorithm and the impact of commonly used features on results. Method: We used a combination of manual validation and adopted or improved heuristics for the collection of defect data. We conducted an empirical study on 398 releases of 38 Apache projects. Results: We found that only half of the bug fixing commits determined by SZZ are actually bug fixing. If a six-month time frame is used in combination with SZZ to determine which bugs affect a release, one file is incorrectly labeled as defective for every file that is correctly labeled as defective. In addition, two defective files are missed. We also explored the impact of the relatively small set of features that are available in most defect prediction data sets, as there are multiple publications that indicate that, e.g., churn related features are important for defect prediction. We found that the difference of using more features is not significant. Conclusion: Problems with inaccurate defect labels are a severe threat to the validity of the state of the art of defect prediction. Small feature sets seem to be a less severe threat.

Steffen Herbold, Aynur Amirfallah, Fabian Trautsch et al.

Developer social networks (DSNs) are a tool for the analysis of community structures and collaborations between developers in software projects and software ecosystems. Within this paper, we present the results of a systematic mapping study on the use of DSNs in software engineering research. We identified 255 primary studies on DSNs. We mapped the primary studies to research directions, collected information about the data sources and the size of the studies, and conducted a bibliometric assessment. We found that nearly half of the research investigates the structure of developer communities. Other frequent topics are prediction systems build using DSNs, collaboration behavior between developers, and the roles of developers. Moreover, we determined that many publications use a small sample size regarding the number of projects, which could be problematic for the external validity of the research. Our study uncovered several open issues in the state of the art, e.g., studying inter-company collaborations, using multiple information sources for DSN research, as well as general lack of reporting guidelines or replication studies.