Tajmilur Rahman

Lamyea Maha, Tajmilur Rahman, Chanchal Roy

The sprint-based iterative approach in the Agile software development method allows continuous feedback and adaptation. One of the crucial Agile software development activities is the sprint planning session where developers estimate the effort required to complete tasks through a consensus-based estimation technique such as Planning Poker. In the Agile software development method, a common unit of measuring development effort is Story Point (SP) which is assigned to tasks to understand the complexity and development time needed to complete them. Despite the benefits of this process, it is an extremely time-consuming manual process. To mitigate this issue, in this study, we investigated if this manual process can be automated using Retrieval Augmented Generation (RAG) which comprises a "Retriever" and a "Generator". We applied two embedding models - bge-large-en-v1.5, and Sentence-Transformers' all-mpnet-base-v2 on 23 open-source software projects of varying sizes and examined four key aspects: 1) how retrieval hyper-parameters influence the performance, 2) whether estimation accuracy differs across different sizes of the projects, 3) whether embedding model choice affects accuracy, and 4) how the RAG-based approach compares to the existing baselines. Although the RAG-based approach outperformed the baseline models in several occasions, our results did not exhibit statistically significant differences in performance across the projects or across the embedding models. This highlights the need for further studies and refinement of the RAG, and model adaptation strategies for better accuracy in automatically estimating user stories.

Tajmilur Rahman, Yuecai Zhu

Modern Software Engineering era is moving fast with the assistance of artificial intelligence (AI), especially Large Language Models (LLM). Researchers have already started automating many parts of the software development workflow. Requirements Engineering (RE) is a crucial phase that begins the software development cycle through multiple discussions on a proposed scope of work documented in different forms. RE phase ends with a list of user-stories for each unit task identified through discussions and usually these are created and tracked on a project management tool such as Jira, AzurDev etc. In this research we developed a tool "GeneUS" using GPT-4.0 to automatically create user stories from requirements document which is the outcome of the RE phase. The output is provided in JSON format leaving the possibilities open for downstream integration to the popular project management tools. Analyzing requirements documents takes significant effort and multiple meetings with stakeholders. We believe, automating this process will certainly reduce additional load off the software engineers, and increase the productivity since they will be able to utilize their time on other prioritized tasks.

Tajmilur Rahman, Rahul Singh, Mir Yousuf Sultan

The advent of Large Language Models (LLMs) has revolutionized various domains of artificial intelligence, including the realm of software engineering. In this research, we evaluate the efficacy of pre-trained LLMs in replicating the tasks traditionally performed by developers in response to code review comments. We provide code contexts to five popular LLMs and obtain the suggested code-changes (patch sets) derived from real-world code-review comments. The performance of each model is meticulously assessed by comparing their generated patch sets against the historical data of human-generated patch-sets from the same repositories. This comparative analysis aims to determine the accuracy, relevance, and depth of the LLMs' feedback, thereby evaluating their readiness to support developers in responding to code-review comments. Novelty: This particular research area is still immature requiring a substantial amount of studies yet to be done. No prior research has compared the performance of existing Large Language Models (LLMs) in code-review comments. This in-progress study assesses current LLMs in code review and paves the way for future advancements in automated code quality assurance, reducing context-switching overhead due to interruptions from code change requests.