When identifying the origin of software bugs, many studies assume that “a bug was introduced by the lines of code that were modified to fix it”. However, this assumption does not always hold and at least in some cases, these modified lines are not responsible for introducing the bug. For example, when the bug was caused by a change in an external API. The lack of empirical evidence makes it impossible to assess how important these cases are and therefore, to which extent the assumption is valid. To advance in this direction, and better understand how bugs “are born”, we propose a model for defining criteria to identify the first snapshot of an evolving software system that exhibits a bug. This model, based on the perfect test idea, decides whether a bug is observed after a change to the software. Furthermore, we studied the model’s criteria by carefully analyzing how 116 bugs were introduced in two different open source software projects. The manual analysis helped classify the root cause of those bugs and created manually curated datasets with bug-introducing changes and with bugs that were not introduced by any change in the source code. Finally, we used these datasets to evaluate the performance of four existing SZZ-based algorithms for detecting bug-introducing changes. We found that SZZ-based algorithms are not very accurate, especially when multiple commits are found; the F-Score varies from 0.44 to 0.77, while the percentage of true positives does not exceed 63%. Our results show empirical evidence that the prevalent assumption, “a bug was introduced by the lines of code that were modified to fix it”, is just one case of how bugs are introduced in a software system. Finding what introduced a bug is not trivial: bugs can be introduced by the developers and be in the code, or be created irrespective of the code. Thus, further research towards a better understanding of the origin of bugs in software projects could help to improve design integration tests and to design other procedures to make software development more robust.

Background: Many studies in the software research literature on bug fixing are built upon the assumption that "a given bug was introduced by the lines of code that were modified to fix it", or variations of it. Although this assumption seems very reasonable at first glance, there is little empirical evidence supporting it. A careful examination surfaces that there are other possible sources for the introduction of bugs such as modifications to those lines that happened before the last change an changes external to the piece of code being fixed. Goal: We aim at understanding the complex phenomenon of bug introduction and bug fix. Method: We design a preliminary approach distinguishing between bug introducing commits (BIC) and first failing moments (FFM). We apply this approach to Nova and ElasticSearch, two large and well-known open source software projects. Results: In our initial results we obtain that at least 24% bug fixes in Nova and 10% in ElasticSearch have not been caused by a BIC but by co-evolution, compatibility issues or bugs in external API. Merely 26--29% of BICs can be found using the algorithm based on the assumption that "a given bug was introduced by the lines of code that were modified to fix it". Conclusions: The approach allows also for a better framing of the comparison of automatic methods to find bug inducting changes. Our results indicate that more attention should be paid to whether a bug has been introduced and, when it was introduced.