Transparent combination of expert and measurement data for defect prediction
| Autor: | Michael Kläs, Klaus Hartjes, Frank Elberzhager, Jürgen Münch, Olaf von Graevemeyer |
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| Rok vydání: | 2010 |
| Předmět: |
FOS: Computer and information sciences
Estimation business.industry Computer science Control (management) Machine learning computer.software_genre Task (project management) Domain (software engineering) Software Engineering (cs.SE) Computer Science - Software Engineering Software Artificial intelligence business Focus (optics) Quality assurance computer |
| Zdroj: | Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2. |
| DOI: | 10.1145/1810295.1810313 |
| Popis: | Defining strategies on how to perform quality assurance (QA) and how to control such activities is a challenging task for organizations developing or maintaining software and software-intensive systems. Planning and adjusting QA activities could benefit from accurate estimations of the expected defect content of relevant artifacts and the effectiveness of important quality assurance activities. Combining expert opinion with commonly available measurement data in a hybrid way promises to overcome the weaknesses of purely data-driven or purely expert-based estimation methods. This article presents a case study of the hybrid estimation method HyDEEP for estimating defect content and QA effectiveness in the telecommunication domain. The specific focus of this case study is the use of the method for gaining quantitative predictions. This aspect has not been empirically analyzed in previous work. Among other things, the results show that for defect content estimation, the method performs significantly better statistically than purely data-based methods, with a relative error of 0.3 on average (MMRE). 10 pages. The final publication is available at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6062145 |
| Databáze: | OpenAIRE |
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