Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?

Abstract

It is popular to use software defect prediction (SDP) techniques to predict bugs in software in the past 20 years. Before conducting software testing (ST), the result of SDP assists on resource allocation for ST. However, DP usually works on fine-level tasks (or white-box testing) instead of coarse-level tasks (or black-box testing). Before ST or without historical execution information, it is difficult to get resource allocated properly. Therefore, a SDP-based approach, named DPAHM, is proposed to assist on arranging resource for coarse-level tasks. The method combines analytic hierarchy process (AHP) and variant incidence matrix. Besides, we apply the proposed DPAHM into a proprietary software, named MC. Besides, we conduct an up-to-down structure, including three layers for MC. Additionally, the performance measure of each layer is calculated based on the SDP result. Therefore, the resource allocation strategy for coarse-level tasks is gained according to the prediction result. The experiment indicates our proposed method is effective for resource allocation of coarse-level tasks before executing ST.