ABSTRACT Software engineering plays a critical role in improving the quality of software systems, because identifying and correcting defects is one of the most expensive tasks in software development life cycle. For instance, determining whether a software product still has defects before distributing it is crucial. The customer's confidence in the software product will decline if the defects are discovered after it has been deployed. Machine learning‐based techniques for predicting software defects have lately started to yield encouraging results. The software defect prediction system's prediction results are raised by machine learning models. More accurate models tend to be more complicated, which makes them harder to interpret. As the rationale behind machine learning models' decisions are obscure, it is challenging to employ them in actual production. In this study, we employ five different machine learning models which are random forest (RF), gradient boosting (GB), naive Bayes (NB), multilayer perceptron (MLP), and neural network (NN) to predict software defects and also provide an explainable artificial intelligence (XAI) framework to both locally and globally increase openness throughout the machine learning pipeline. While global explanations identify general trends and feature importance, local explanations provide insights into individual instances, and their combination allows for a holistic understanding of the model. This is accomplished through the utilization of Explainable AI algorithms, which aim to reduce the “black‐boxiness” of ML models by explaining the reasoning behind a prediction. The explanations provide quantifiable information about the characteristics that affect defect prediction. These justifications are produced using six XAI methods, namely, SHAP, anchor, ELI5, LIME, partial dependence plot (PDP), and ProtoDash. We use the KC2 dataset to apply these methods to the software defect prediction (SDP) system, and provide and discuss the results.

As the number of available Open Source Software (OSS) and the interest they attract are increasing, numerous product attributes are provided to developers and users for evaluating the quality and success of an OSS. Accordingly, various articles in the literature assess the quality and success of OSS, by using different quality attributes and metrics and different approaches. Though this variety can be considered as a positive indicator of research interest and maturation on one side, it creates a kind of jungle in defining and understanding the terms 'quality' and 'success' on the other side. Based on this challenge, in this study, we targeted a systematic mapping (SM) of the articles on quality and success of OSS. More than 474 articles have appeared in this area between the years 2002 and 2017, and the final pool of 128 articles is obtained by defining and applying inclusion and exclusion criteria. SM was employed to develop a classification scheme and categorized the existing body of articles with respect to five research questions (RQs) on: contribution and research types, quality criteria and metrics, success criteria and metrics, the relation of quality and success, and demographics. We observed that the majority of the articles assess the concept of quality as 'code quality', whereas the concept of success is mostly perceived as 'market success' and/or 'developer activity'. Moreover, the metrics of 'contributing developers/users', and the quality attribute of 'functionality' are the quality criteria most employed in the assessment of success.