A unified platform that utilizes multiple artificial intelligence agents to automate the process of transforming user requirements into well-organized deliverables, including user stories, prioritization, and UML sequence diagrams, along with the modular approach to APIs, unit tests, and end-to-end tests.
Malik Abdul Sami, Muhammad Waseem, Z. Rasheed et al.· arXiv.org· 14 citations· ⚡1
A web-based software tool is introduced that employs an LLM-based agent and prompt engineering to automate the generation of test case scenarios against user requirements and crafting test case scenarios based on these stories.
Malik Abdul Sami, Z. Rasheed, Muhammad Waseem et al.· arXiv.org· 13 citations
A web-based software tool utilizing AI agents and prompt engineering to automate task prioritization and apply diverse prioritization techniques, aimed at enhancing project management within the agile framework is introduced.
Malik Abdul Sami, Z. Rasheed, Muhammad Waseem et al.· arXiv.org· 15 citations
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
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Results corroborate the effectiveness of LLMs in improving and streamlining RE phases by analyzing the semantic similarity and API performance of different models, as well as their effectiveness and efficiency in requirements analysis.
Malik Abdul Sami, Muhammad Waseem, Zheying Zhang et al.· arXiv.org· 17 citations· ⚡2
A novel multi-agent AI model is introduced that aims to assess and compare the performance of various LLMs, and initial results indicate that the GPT-3.5 Turbo model's performance is comparatively better than the other models.
Z. Rasheed, Muhammad Waseem, Kari Systä et al.· arXiv.org· 23 citations
It is shown in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars.
Z. Rasheed, Muhammad Waseem, Mika Saari et al.· arXiv.org· 20 citations· ⚡1
The results indicate that if the OpenAPI specification is kept small and focused, LLM-based multi-agent systems are capable of generating complete functional code with business logic that aligns to the specification.
Saurabh Chauhan, Z. Rasheed, Malik Abdul Sami et al.· arXiv.org· 1 citation
This work proposes and develops a multi-model unified platform to generate and execute code based on natural language prompts and presents practitioners feedback and insights into the use of LLMs in software development, including their strengths and weaknesses, key aspects overlooked by benchmarks and metrics.
Z. Rasheed, Muhammad Waseem, Kai-Kristian Kemell et al.· arXiv.org· 18 citations· ⚡2
A system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices and assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages is presented.
Saurabh Chauhan, Z. Rasheed, Malik Abdul Sami et al.· International Conference on...· 16 citations· ⚡1
A large language models based multi-agent system enables precise task execution and inter-agent collaboration, addressing the challenges of refactoring in functional programming.
Shahbaz Siddeeq, Z. Rasheed, Malik Abdul Sami et al.· arXiv.org· 1 citation
This study presents a comprehensive empirical evaluation of 20 open-source Small Language Models and reveals that several compact SLMs achieve competitive results while maintaining a balance between performance and efficiency, making them viable for deployment in resource-constrained environments.
Mahade Hasan, Muhammad Waseem, Kai-Kristian Kemell et al.· Journal of Systems and Softw...· 16 citations
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
