Machine Learning And Deep Learning Based Approaches For Detecting Duplicate Bug Reports With Stack Traces

Many large software systems rely on bug tracking systems to record the submitted bug reports and to track and manage bugs. Handling bug reports is known to be a challenging task, especially in software organizations with a large client base, which tend to receive a considerable large number of bug reports a day. Fortunately, not all reported bugs are new; many are similar or identical to previously reported bugs, also called duplicate bug reports. Automatic detection of duplicate bug reports is an important research topic to help reduce the time and effort spent by triaging and development teams on sorting and fixing bugs. This explains the recent increase in attention to this topic as evidenced by the number of tools and algorithms that have been proposed in academia and industry. The objective is to automatically detect duplicate bug reports as soon as they arrive into the system. To do so, existing techniques rely heavily on the nature of bug report data they operate on. This includes both structural information such as OS, product version, time and date of the crash, and stack traces, as well as unstructured information such as bug report summaries and descriptions written in natural language by end users and developers

Automatic sentence annotation for more useful bug report summarization

Bug reports are a useful software artifact with software developers referring to them for various information needs. As bug reports can become long, users of bug reports may need to spend a lot of time reading them. Previous studies developed summarizers and the quality of summaries was determined based on human-created gold-standard summaries. We believe creating such summaries for evaluating summarizers is not a good practice. First, we have observed a high level of disagreement between the annotated summaries. Second, the number of annotators involved is lower than the established minimum for the creation of a stable annotated summary. Finally, the traditional fixed threshold of 25% of the bug report word count does not adequately serve the different information needs. Consequently, we developed an automatic sentence annotation method to identify content in bug report comments which allows bug report users to customize a view for their task-dependent information needs