Software repositories contain a wealth of data about the software development process, such as source code, documentation, issue tracking, and commit histories. However, accessing and extracting meaningful insights from these data is timeconsuming and requires technical expertise, posing challenges for software practitioners, especially non-technical stakeholders like project managers. Existing solutions, such as software engineering chatbots leveraging LLMs, have demonstrated significant limitations in retrieving relevant data to answer user questions. In this paper, we introduce RepoChat, a web-based tool designed to answer repository-related questions by synergizing LLMs with knowledge graphs. RepoChat operates in two steps: (1) the Data Ingestion step, where it collects and constructs a knowledge graph from repository metadata, such as commits, issues, files and users; and (2) the Interaction step, where it takes the users natural language question, translates it into graph queries using an LLM, executes these queries against the knowledge graph, and generates a user-friendly response to the question using the query results as context. We evaluate RepoChat by conducting a user study in which participants asked a series of repository-related questions representing common developer intents. RepoChat achieved an accuracy of $90 \%$, correctly answering 36 out of 40 questions, demonstrating its effectiveness in accurately retrieving relevant information to answer user’s questions. RepoChat is available at https://repochattool.streamlit.app/, and its source code is accessible on Zenodo [1].

Effective model management is crucial for ensuring performance and reliability in Machine Learning (ML) systems, given the dynamic nature of data and operational environments. However, standard practices are lacking, often resulting in ad hoc approaches. To address this, our research provides a clear definition of ML model management activities, processes, and techniques. Analyzing 227 ML repositories, we propose a taxonomy of 16 model management activities and identify 12 unique challenges. We find that 57.9% of the identified activities belong to the maintenance category, with activities like refactoring (20.5%) and documentation (18.3%) dominating. Our findings also reveal significant challenges in documentation maintenance (15.3%) and bug management (14.9%), emphasizing the need for robust versioning tools and practices in the ML pipeline. Additionally, we conducted a survey that underscores a shift toward automation, particularly in data, model, and documentation versioning, as key to managing ML models effectively. Our contributions include a detailed taxonomy of model management activities, a mapping of challenges to these activities, practitioner-informed solutions for challenge mitigation, and a publicly available dataset of model management activities and challenges. This work aims to equip ML developers with knowledge and best practices essential for the robust management of ML models.

Software repositories have a plethora of information about software development, encompassing details such as code contributions, bug reports and code reviews. This rich source of data can be harnessed to enhance not only software quality and development velocity but also to gain insights into team collaboration and inform strategic decision-making throughout the software development lifecycle. Previous studies show that many stakeholders cannot benefit from the project information due to the technical knowledge and expertise required to extract the project data.

Context: There has been a growing research focus in conventional machine learning techniques for software effort estimation (SEE).However, there is a limited number of studies that seek to assess the performance of deep learning approaches in SEE.This is because the sizes of SEE datasets are relatively small.Purpose: This study seeks to define a threshold for small-sized datasets in SEE, and investigates the performance of selected conventional machine learning and deep learning models on small-sized datasets.Method: Plausible SEE datasets with their number of project instances and features are extracted from existing literature and ranked.Eubank's optimal spacing theory is used to discretize the ranking of the project instances into three classes (small, medium and large).Five conventional machine learning models and two deep learning models are trained on each dataset classified as small-sized using the leave-one-out cross-validation.The mean absolute error is used to assess the prediction performance of each model.Result: Findings from the study contradicts existing knowledge by demonstrating that deep learning models provide improved prediction performance as compared to the conventional machine learning models on small-sized datasets.Conclusion: Deep learning can be adopted for SEE with the application of regularisation techniques.