Lately, software development has become a predominantly online process, as more teams host and monitor their projects remotely. Sophisticated approaches employ issue tracking systems like Jira, predicting the time required to resolve issues and effectively assigning and prioritizing project tasks. Several methods have been developed to address this challenge, widely known as bug-fix time prediction, yet they exhibit significant limitations. Most consider only textual issue data and/or use techniques that overlook the semantics and metadata of issues (e.g., priority or assignee expertise). Many also fail to distinguish actual development effort from administrative delays, including assignment and review phases, leading to estimates that do not reflect the true effort needed. In this work, we build an issue monitoring system that extracts the actual effort required to fix issues on a per-project basis. Our approach employs topic modeling to capture issue semantics and leverages metadata (components, labels, priority, issue type, assignees) for interpretable resolution time analysis. Final predictions are generated by an aggregated model, enabling contributors to make informed decisions. Evaluation across multiple projects shows the system can effectively estimate resolution time and provide valuable insights.

The continuously improving capabilities of Artificial Intelligence (AI) systems are rapidly establishing them as the de facto choice for complex tasks and sophisticated reasoning. Evaluation tasks, for example, are particularly challenging, since they demand expert judgment, contextual analysis, and nuanced decision-making. Educational assessment represents a critical instance of such a complex cognitive task, where scalability challenges force institutions to choose between efficiency and assessment quality, as enrollment outpaces faculty capacity. While large language models seem promising for assisting in this context, they often lack domain-specific knowledge and pedagogical context, which are essential for effective assessment. This paper presents a systematic methodology for human-AI collaboration that addresses these limitations and achieves scalable efficiency, while preserving instructor autonomy. We validate our approach in the Software Engineering education domain, an especially demanding testbed, which requires assessment across multiple technical artifacts that combine objective correctness with subjective design quality. The system is tested against 30 software engineering projects, across different software engineering artifact types. Our evaluation demonstrates significant efficiency improvement against the current (manual) assessment approach, indicating that the systematic provision of domain knowledge can enable AI assistance in complex educational evaluation tasks.

Developers nowadays are increasingly turning to large language models (LLMs) like ChatGPT to assist them with coding tasks, inspired by the promise of efficiency and the advanced capabilities they offer. However, this raises important questions about the ease of integration and the safety of incorporating these tools into the development process. To investigate these questions, this paper examines a set of ChatGPT conversations. Upon annotating the conversations according to the intent of the developer, we focus on two critical aspects: firstly, the ease with which developers can produce suitable source code using ChatGPT, and, secondly, the quality aspects of the generated source code, determined by the compliance to standards and best practices. We research both the quality of the generated code itself and its impact on the project of the developer. Our results indicate that ChatGPT can be a useful tool for software development when used with discretion.

Current state of practice dictates that software developers host their projects online and employ project management systems to monitor the development of product features, keep track of bugs, and prioritize task assignments. The data stored in these systems, if their semantics are extracted effectively, can be used to answer several interesting questions, such as finding who is the most suitable developer for a task, what the priority of a task should be, or even what is the actual workload of the software team. To support researchers and practitioners that work towards these directions, we have built a system that crawls data from the Jira management system, performs topic modeling on the data to extract useful semantics and stores them in a practical database schema. We have used our system to retrieve and analyze 656 projects of the Apache Software Foundation, comprising data from more than a million Jira issues.