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.

Map-Reduce is a programming model and execution an environment developed by Google to process very large amounts of data. Query optimizer is needed to find more efficient plans for declarative SQL query. In classic database: join algorithms are optimized to execute the entire query result, but they ignore the importance of tables order especially in multi-join query. But we can see that the orders for tables are an important factor to get the best performance of a query plan and will be very effective in performance when join tables content huge number of rows in addition to more than one join operation. In this paper we proposed a new technique called JOMR (Join Order In Map-Reduce) that optimizes and enhances Map-Reduce job. This technique uses enhanced parallel Travel Salesman Problem (TSP) using Map-Reduce for improving the performance of query plans according to change the order for join tables. Also we build a cost model that supports our algorithm to find best join order. We will focus on Hive especially multi-join query and our experiments result for JOMR algorithm proving the effectiveness of our query optimizer and this performance is improved more when increasing the number of join and size of data.

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 the suitable developer to new bug by analyzing the history of developers’ profiles and analyzing 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 can't 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. In this paper, we propose two explainable models for recommendation. The first one 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 generate the best developer for each bug from bug history.