Software engineering is a comprehensive process that requires developers and team members to collaborate across multiple tasks. In software testing, bug triaging is a tedious and time-consuming process. Assigning bugs to the appropriate developers can save time and maintain their motivation. However, without knowledge about a bug’s class, triaging is difficult. Motivated by this challenge, this paper focuses on the problem of assigning a suitable developer to a new bug by analyzing the history of developers’ profiles and analyzing the history of bugs for all developers using machine learning-based recommender systems. Explainable AI (XAI) is AI that humans can understand. It contrasts with “black box” AI, which even its designers cannot explain. By providing appropriate explanations for results, users can better comprehend the underlying insight behind the outcomes, boosting the recommender system’s effectiveness, transparency, and confidence. The trained model is utilized in the recommendation stage to calculate relevance scores for developers based on expertise and past bug handling performance, ultimately presenting the developers with the highest scores as recommendations for new bugs. This approach aims to strike a balance between computational efficiency and accurate predictions, enabling efficient bug assignment while considering developer expertise and historical performance. In this paper, we propose two explainable models for recommendation. The first is an explainable recommender model for personalized developers generated from bug history to know what the preferred type of bug is for each developer. The second model is an explainable recommender model based on bugs to identify the most suitable developer for each bug from bug history.

Most enterprises operate within a complex and ever-changing context. To ensure that requirements keep pace with changing context, users’ feedback is advocated to ensure that the requirements knowledge is refreshed and reflects the degree to which the system meets its design objectives. The traditional approach to users’ feedback, which is based on data mining and text analysis, is often limited, partly due to the ad-hoc nature of users’ feedback and, also, the methods used to acquire it. To maximize the expressiveness of users’ feedback and still be able to efficiently analyse it, we propose that feedback acquisition should be designed with that goal in mind. This paper contributes to that aim by presenting an empirical study that investigates users’ perspectives on feedback constituents and how they could be structured. This will provide a baseline for modelling and customizing feedback for enterprise systems in order to maintain and evolve their requirements.

This research aims to enhance e-recruitment systems using text summarization techniques and pretrained large language models (LLMs). A job recommender system is built with integrated text summarization. The text summarization techniques that are selected are BART, T5 (Text-to-Text Transfer Transformer), BERT, and Pegasus. Content-based recommendation is the model chosen to be implemented. The LinkedIn Job Postings dataset is used. The evaluation of the text summarization techniques is performed using ROUGE-1, ROUGE-2, and ROUGE-L. The results of this approach deduce that the recommendation does improve after text summarization. BERT outperforms other summarization techniques. Recommendation evaluations show that, for MRR, BERT performs 256.44% better, indicating relevant recommendations at the top more effectively. For RMSE, there is a 29.29% boost, indicating recommendations closer to the actual values. For MAP, a 106.46% enhancement is achieved, presenting the highest precision in recommendations. Lastly, for NDCG, there is an 83.94% increase, signifying that the most relevant recommendations are ranked higher.

Gamification has become a motivational technique for enhancing engagement and productivity, extending into agile software development. However, integrating gamification into agile frameworks such as the Scrum framework has led to the emergence of gamification risks, which may have adverse impacts on agile roles and tasks. These risks include an increase in the number of unassigned tasks affecting sprint velocity, novelty-seeking and quick boredom, clustering group, and intimidation, thus showing the need for a structured approach toward their management, their impacts on team dynamics and project outcomes. This study proposes the Agile Gamification Risk Management (AGRM) process, focused on identifying, assessing, and mitigating gamification risks in agile software enterprises. AGRM introduces artifacts such as the Gamification Risk Reporting Form, Personalized Risk Profiles, Task Impact Matrix, and Gamification Risk Register, enabling real-time proactive risk management. By leveraging a gamification risk taxonomy, AGRM categorizes and prioritizes risks, aligning mitigation efforts effectively. This paper details a two-phased empirical study to evaluate our proposed AGRM process. The proposed process identified 17 and mitigated 9 gamification risks for two agile teams in two software development enterprises. Unlike ad hoc practices, AGRM provides a structured approach, empowering teams to manage risks during sprint events. By incorporating artifacts like the Gamification Risk Register (GRR) and Personalized Risk Profiles (PRPs), teams can assess risks in context, enhancing productivity, collaboration, and project outcomes. The results demonstrate AGRM’s ability to boost team morale and confidence in addressing gamification risks effectively.