Defect Prediction: SPE

Software process evaluation is essential to improve software development and the quality of software products in an organization. Conventional approaches based on manual qualitative evaluations (e.g., artifacts inspection) are deficient in the sense that (i) they are time-consuming, (ii) they suffer from the authority constraints, and (iii) they are often subjective. To overcome these limitations, this paper presents a novel semi-automated approach to software process evaluation using machine learning techniques. In particular, we formulate the problem as a sequence classification task, which is solved by applying machine learning algorithms. Based on the framework, we define a new quantitative indicator to objectively evaluate the quality and performance of a software process. To validate the efficacy of our approach, we apply it to evaluate the defect management process performed in four real industrial software projects. Our empirical results show that our approach is effective and promising in providing an objective and quantitative measurement for software process evaluation.

Reference: Chen, Ning, Steven CH Hoi, and Xiaokui Xiao. "Software process evaluation: A machine learning approach." Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering. IEEE Computer Society, 2011.

软件过程评估对于提升组织内的软件开发效率与软件产品质量至关重要。传统基于人工定性评估（如工件评审（artifacts inspection））的方法存在诸多不足：其一，耗时耗力；其二，受限于权威约束；其三，往往带有主观性。为克服上述不足，本文提出一种基于机器学习技术的新型半自动化软件过程评估方法。具体而言，我们将该问题建模为序列分类任务（sequence classification task），并通过机器学习算法予以求解。基于该框架，我们定义了一种全新的量化指标，用于客观评估软件过程的质量与性能。为验证所提方法的有效性，我们将其应用于四个真实工业软件项目中的缺陷管理流程（defect management process）评估。实验结果表明，该方法能够为软件过程评估提供客观且量化的度量手段，具备良好的有效性与应用前景。

参考文献：陈宁、Steven CH Hoi、肖小葵. 软件过程评估：一种机器学习方法[C]//2011年第26届IEEE/ACM自动化软件工程国际会议论文集. IEEE计算机协会，2011.