Abstract:
In recent years, it has been shown that fault prediction models could effectively guide test effort allocation in finding faults if they have a high enough fault predicti...Show MoreMetadata
Abstract:
In recent years, it has been shown that fault prediction models could effectively guide test effort allocation in finding faults if they have a high enough fault prediction accuracy (Norm(Popt) > 0.78). However, it is often difficult to achieve such a high fault prediction accuracy in practice. As a result, fault-prediction-model-guided allocation (FPA) methods may be not applicable in real development environments. To attack this problem, in this paper, we propose a new type of test effort allocation strategy: reliability-growth-model-guided allocation (RGA) method. For a given project release V, RGA attempts to predict the optimal test effort allocation for V by learning the fault distribution information from the previous releases. Based on three open-source projects, we empirically investigate the cost-effectiveness of three test effort allocation strategies for finding faults: RGA, FPA, and structural-complexity-guided allocation (SCA) method. The experimental results show that RGA shows a promising performance in finding faults when compared with SCA and FPA.
Published in: 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)
Date of Conference: 20-24 February 2017
Date Added to IEEE Xplore: 23 March 2017
ISBN Information: