Quantifying Value Of Adding Inspection Effort Early In The Development Process: A Case Study

Software, IET(2012)

引用 8|浏览3
暂无评分
摘要
Many researchers have reported the defect growth within the evolutionary-developed large-scale systems, and increased fault slips from the early verification stages into late. This suggests that improvement in the early defect detection process control is needed. This study focuses on evaluation of adding inspection effort early in the development process. Based on the examination of the existing metrics used in defect detection process, the authors establish metrics to quantify its value from the quality and cost-benefit perspective. The effect of adding inspection effort early in the development process is evaluated in a case study using industrial data from history and an ongoing project involving three geographically distributed sites of the same globally distributed software development organisation with around 300 developers. The findings show that the expert-based decision criteria for additional investment are mostly based on quality and reliability issues, and less on costs. Consequently, the additional inspection improves significantly the quality, while the cost-benefit was not statistically significant. This leads to the conclusion that better decision criteria that would incorporate the costs and not only quality perceptions are the key for improving the product reliability, as well as the overall software life-cycle cost-efficiency. This study is motivated by the real industrial environment, and thus, contributes to both research and practice by presenting the empirical evidence.
更多
查看译文
关键词
fault tolerant computing,software cost estimation,software quality,software reliability,cost-benefit perspective,defect detection process,evolutionary-developed large-scale system,expert-based decision criteria,fault slips,geographically distributed sites,globally distributed software development organisation,product reliability,quality perspective,software life-cycle cost-efficiency
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要