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Babar Shah

Publications and source records attributed to Babar Shah.

2 recordsLinked to original sources

Expertise-Based Developer Assignment for Long-Term Software Components in Open-Source Projects

Open-source software development through GitHub has enabled countless software projects to be developed by developers from across the world. Assigning the right task to the right developer enables teams to work efficiently. Assigning a task which is not representative of a developer's expertise results in slower completion time and less maintainable code. Whilst much work has been done to automate this assignment for specific jobs, there is little work addressing the assignment of developers to expertise-specific long-term components of a new project. This paper produces a web application that models developers' expertise based on their previous Git commits and automatically assigns them to an optimal task within a new project. Testing showed that the system's speed varies depending on the back-end model used but server-side data caching improved worst-case speeds by a factor of 9.86. 72.4% of tasks had their target developer listed in the top 10, out of a possible 47.

cs.SE

Analysing User Reviews to Identify User Concerns Around Permissions in AI Apps

Artificial intelligence is increasingly embedded in everyday software, making its integration into mobile apps inevitable. However, AI mobile app developers are not always versed in security and privacy best practices, leaving users to monitor their own security and understand how apps use their data. App reviews capture real user experiences, helping others make informed decisions before downloading. This paper presents a machine learning model for classifying AI app reviews into permission-related categories. Because user reviews are unstructured, assembling a conventional labeled training set is difficult. To address this, AI-generated security and permission reviews are used to identify relevant training examples from a large corpus of human-written reviews, eliminating the need for manual annotation. The proposed approach classified permission reviews with an accuracy of 82%. Analysis shows that users organise their concerns by sentiment toward the requesting app rather than specific permission types, with implications for users, developers, and platform administrators.

cs.LG