Transparent Combination of Expert and Measurement Data for Defect Prediction: An Industrial Case Study
This addresses the challenge of planning quality assurance activities for organizations in the telecommunication domain, though it is incremental as it builds on prior work by focusing on quantitative predictions.
The paper tackles the problem of estimating defect content and quality assurance effectiveness in software development by combining expert opinion with measurement data, showing that their hybrid method HyDEEP performs significantly better than purely data-based methods with an average relative error of 0.3 (MMRE).
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).