Automated support of software quality improvement

Abstract

Purpose

Quality improvement is critical to the success of software development organizations. Automated tools can greatly assist in the implementation of quality improvement. The purpose of this paper is to investigate the state of tool support of software process improvement.

Design/methodology/approach

The authors first review three quality improvement models that can be used for improving the quality of software products, and then identify the required activities of the quality improvement effort based on those models. These activities identified then serve as a reference list to determine the support provided by commercial tools. The paper then conducts two surveys of software quality improvement tools and presents the results.

Findings

The results from two surveys indicate that: there is no tool that supports problem identification; most tools can provide several software process improvement functions to help software organizations to improve their process capability and the quality of their products; and there is no set of tools that can provide a complete coverage of all improvement activities.

Research limitations/implications

Although the authors have conducted a detailed search on the internet to identify SPI tools, such a survey will miss those tools which have no presence on the internet. However, the authors are confident that their results can reflect what is available in the marketplace.

Practical implications

Process practitioners will learn about the state of support available in the marketplace and what they may need to develop on their own to provide complete support for their improvement efforts.

Originality/value

The paper proposes a classification of software process improvement functions and present a first survey of software quality improvement tools. The results will be beneficial to process practitioners.

Citation

Leung, H.K.N., Liao, L. and Qu, Y. (2007), "Automated support of software quality improvement", International Journal of Quality & Reliability Management, Vol. 24 No. 3, pp. 230-243. https://doi.org/10.1108/02656710710730843