SQAPlanner: Generating Data-Informed Software Quality Improvement Plans

Abstract

Software Quality Assurance (SQA) planning aims to define proactive plans, such as defining maximum file size, to prevent the occurrence of software defects in future releases. To aid this, defect prediction models have been proposed to generate insights as the most important factors that are associated with software quality. Such insights that are derived from traditional defect models are far from actionable-i.e., practitioners still do not know what they should do or avoid to decrease the risk of having defects, and what is the risk threshold for each metric. A lack of actionable guidance and risk threshold can lead to inefficient and ineffective SQA planning processes. In this paper, we investigate the practitioners' perceptions of current SQA planning activities, current challenges of such SQA planning activities, and propose four types of guidance to support SQA planning. We then propose and evaluate our AI-Driven SQAPlanner approach, a novel approach for generating four types of guidance and their associated risk thresholds in the form of rulebased explanations for the predictions of defect prediction models. Finally, we develop and evaluate an information visualization for our SQAPlanner approach. Through the use of qualitative survey and empirical evaluation, our results lead us to conclude that SQAPlanner is needed, effective, stable, and practically applicable. We also find that 80% of our survey respondents perceived that our visualization is more actionable. Thus, our SQAPlanner paves a way for novel research in actionable software analytics-i.e., generating actionable guidance on what should practitioners do and not do to decrease the risk of having defects to support SQA planning.

Deep Learning for Android Malware Defenses: a Systematic Literature Review

“…Interpretable or explainable deep learning based Android malware analysis is also a future interesting topic [60,160]. Recently, researchers have focused on conducting empirical studies to highlight the need of explainable AI/ML models for software engineering [75] and developing novel approaches for explainable AI/ML models for software engineering [75,83,125,125,129,135,176]. Although existing studies have attempted to employ local explainable approaches to provide explanations based on the Android characteristic-based features for each unknown sample [179], there are still several issues requiring further exploration.…”