Predicting just-in-time software defects to reduce post-release quality costs in the maritime industry

Background: The importance of software in maritime transportation is rapidly increasing as the industry seeks to develop and utilize innovative future ships, which can be realized using software technology. Due to the safety-critical nature of ships, software quality assurance (SQA) has become an essential prerequisite for such development. Objective: Based on the unique characteristics of the maritime domain, the purpose of this study was to achieve effective SQA resource allocation to reduce post-release quality costs. Method: Software defect prediction (SDP) is employed to predict defects in newly developed software based on models trained with past software defects and to update information using machine learning. This study demonstrated that just-in-time SDP is applicable to maritime domain practice and can reduce post-release quality costs via combination with an estimation model, qCOPLIMO. Results: Using real-world datasets collected from the maritime industry, performance and cost-benefit analyses of SDP were performed. A successful model was obtained that meets the performance criterion of 0.75 in within-project defect prediction (WPDP) but not cross-project defect prediction (CPDP). In addition, the cost-benefit analysis results showed that 20% effort enables the detection of 56% of defects on average and that the post-release quality cost can be reduced by 37.3% in the maritime domain. Conclusion: SDP can be successfully applied to the maritime domain. Further, it is desirable to utilize WPDP instead of CPDP once minimum high-quality commits are available that can be identified as defective or not. Finally, SDP can help reduce review effort and post-release quality costs.