Journal of Software-Evolution and Process最新文献

Crowdsourcing testing has the advantages of efficiency, speed, and reliability, but an excessive number of test reports makes it a challenge for report reviewers to select high-quality test reports in a limited time. Test reports submitted by crowd workers often tend to be short textual descriptions with a large number of screenshots attached. Most traditional processing methods of test reports target reports that only contain text information, which cannot meet the defect detection requirements of crowdsourced test reports. In view of this, this paper proposes a prioritization method of crowdsourced test reports that integrates text and image information. First, we extract the text and image information from the test reports, based on which the defect detection abilities of the test reports are measured and the similarities between test reports are calculated. Then, a multi-stage prioritization method of the test reports is presented based on the defect detection levels and similarities of the test reports. In the first stage, based on the defect detection levels and the similarities, the test report set is sorted and clustered to obtain the sorting results of partial reports and the similar set for each sorted report; in the second stage, the similar test report set is sorted with the criteria of minimizing the similarity and maximizing the defect detection level; the sorting results of the two stages are combined to form the final priorities of test reports. To validate our approach, we conducted experiments on five crowdsourced test datasets. The results and the analysis show that our approach can detect all faults faster in a limited time. By comprehensively utilizing text and image information to prioritize test reports, better sorting results can be obtained than state-of-the-art methods.

Code readability is of central concern for developers, as a more readable code indicates higher maintainability, reusability, and portability. In recent years, many deep learning–based code readability classification methods have been proposed. Among them, a graph neural network (GNN)–based model has achieved the best performance in the field of code readability classification. However, it is still unclear what aspects of the model's input lead to its decisions, which hinders its practical use in the software industry. To improve the interpretability of existing code readability classification models and identify key code characteristics that drive their readability predictions, we propose an explanation framework with GNN explainers towards transparent and trustworthy code readability classification. First, we propose a simplified Abstract Syntax Tree (AST)–based code representation method, which transforms Java code snippets into ASTs and discards lower-level nodes with limited information. Then, we retrain the state-of-the-art GNN-based model together with our simplified program graphs. Finally, we employ SubgraphX to explain the model's code readability predictions at the subgraph level and visualize the explanation results to further analyze what causes such predictions. The experimental results show that sequential logic, code comments, selection logic, and nested structure are the most influential code characteristics when classifying code snippets as readable or unreadable. Further investigations indicate the model's proficiency in capturing features related to complex logic structures and extensive data flows but point to its limitations in identifying readability issues associated with naming conventions and code formatting. The explainability analysis conducted in this research is the first step towards more transparent and reliable code readability classification. We believe that our findings are useful in providing constructive suggestions for developers to write more readable code and delimitating directions for future model improvement.

The increasing complexity of software development processes has heightened the need for robust security measures. Although technical safeguards are essential, the role of human factors in securing software development remains underexplored. This paper presents a novel approach that integrates people's maturity with a fuzzy analytic hierarchy process (Fuzzy-AHP) decision-making framework to enhance the security in software development. The framework provides a systematic method for evaluating and prioritizing human factors that influence an organization's security posture, such as team-expertized communication and adherence to security protocols. Using the decision-making model allows the project managers and stakeholders to determine the appropriate areas for improvement and develop the right strategies and actions to nurture a secure and mature development culture. The paper identifies 24 human success factors (HSFs) and human security vulnerabilities (HSVs) and 38 practices for addressing these HSFs and HSVs through systematic literature review (SLR) and empirical survey. Furthermore, we discuss the local and global ranks of each HSF and HSV practice and categorize the identified practices into nine categories to determine the ranks and weight of each category. Based on collected data, Fuzzy-AHP prioritized these practices; the category “C4: Skill development and stakeholder engagement” is ranked highest at rank-1 and possesses the most significant weight of 0.12435. Similarly, the highest global weight is 0.051506, and the global ranked (rank-1) HSF and HSV practice is “P15: Hands-on practice and stakeholder communication.” The proposed approach complements existing technical methods by addressing the human element of security, making it adaptable to diverse organizational environments. Through this integration of people maturity and Fuzzy-AHP, the paper contributes a new dimension to securing software development, emphasizing the critical role of human factors in achieving comprehensive security.