Using Categorical Features in Mining Bug Tracking Systems to Assign Bug Reports
This addresses bug assignment in software engineering, offering an incremental improvement by combining categorical features with textual contents.
The paper tackles the problem of low accuracy and high computational needs in bug report assignment by investigating whether categorical features (like component) can effectively represent bug reports instead of noisy textual descriptions. The result shows that categorical features improve classification accuracy, with experiments on NetBeans, Freedesktop, and Firefox projects comparing against two machine learning approaches.
Most bug assignment approaches utilize text classification and information retrieval techniques. These approaches use the textual contents of bug reports to build recommendation models. The textual contents of bug reports are usually of high dimension and noisy source of information. These approaches suffer from low accuracy and high computational needs. In this paper, we investigate whether using categorical fields of bug reports, such as component to which the bug belongs, are appropriate to represent bug reports instead of textual description. We build a classification model by utilizing the categorical features, as a representation, for the bug report. The experimental evaluation is conducted using three projects namely NetBeans, Freedesktop, and Firefox. We compared this approach with two machine learning based bug assignment approaches. The evaluation shows that using the textual contents of bug reports is important. In addition, it shows that the categorical features can improve the classification accuracy.