Assessment and cross-product prediction of software product line quality: accounting for reuse across products, over multiple releases

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• 作者：Thomas Devine ; Katerina Goseva-Popstojanova…
• 关键词：Software product lines ; Cross ; product prediction ; Cross ; product reuse ; Cross ; release reuse ; Longitudinal study ; Assessment ; Fault proneness prediction
• 刊名：Automated Software Engineering
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：23
• 期：2
• 页码：253-302
• 全文大小：2,200 KB
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• 作者单位：Thomas Devine (1)
  Katerina Goseva-Popstojanova (1)
  Sandeep Krishnan (2)
  Robyn R. Lutz (2)
  
  1. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA
  2. Department of Computer Science, Iowa State University, Ames, IA, USA
  
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Software Engineering, Programming and Operating Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7535

文摘

The goals of cross-product reuse in a software product line (SPL) are to mitigate production costs and improve the quality. In addition to reuse across products, due to the evolutionary development process, a SPL also exhibits reuse across releases. In this paper, we empirically explore how the two types of reuse—reuse across products and reuse across releases—affect the quality of a SPL and our ability to accurately predict fault proneness. We measure the quality in terms of post-release faults and consider different levels of reuse across products (i.e., common, high-reuse variation, low-reuse variation, and single-use packages), over multiple releases. Assessment results showed that quality improved for common, low-reuse variation, and single-use packages as they evolved across releases. Surprisingly, within each release, among preexisting (‘old’) packages, the cross-product reuse did not affect the change and fault proneness. Cross-product predictions based on pre-release data accurately ranked the packages according to their post-release faults and predicted the 20 % most faulty packages. The predictions benefited from data available for other products in the product line, with models producing better results (1) when making predictions on smaller products (consisting mostly of common packages) rather than on larger products and (2) when trained on larger products rather than on smaller products.