Publication Type

Conference Proceeding Article

Publication Date

11-2011

Abstract

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.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE): 6-10 November 2011, Lawrence, KS: Proceedings

First Page

333

Last Page

342

ISBN

9781457716386

Identifier

10.1109/ASE.2011.6100070

Publisher

IEEE

City or Country

Piscataway, NJ

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1109/ASE.2011.6100070

Share

COinS