This paper study software development in startups from the point of view of practices to better understand how startups develop software, and devise a list of practices which are categorized using the Essence Theory of Software Engineering (Essence).
Kai-Kristian Kemell, Ville Ravaska, Anh Nguyen-Duc et al.· International Conference on...· 5 citations
This study investigates the tactics behind software engineering (SE) activities by analyzing key engineering events during startup journeys and explores how entrepreneurial mindsets may be associated with SE knowledge areas and with each startup case.
Anh Nguyen-Duc, Kai-Kristian Kemell, P. Abrahamsson· Empirical Software Engineeri...· 23 citations· ⚡1
The first published version of StartCards is presented, which is considered useful for early-stage startups and can also be used as a pedagogical tool in startup education.
Kai-Kristian Kemell, Anh Nguyen-Duc, Mari Suoranta et al.· Information and Software Tec...· 25 citations· ⚡1
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The Second International Workshop on Requirement Engineering for Software startups and Emerging Technologies (RESET) brought together requirements engineering researchers and practitioners to discuss the need for adapting conventional requirement engineering artifacts in developing and operating emerging technologies.
Anh Nguyen-Duc, Chetan Arora, P. Abrahamsson· 2023 IEEE 31st International...· 1 citation
Investigating the impacts of COVID-19 on software development activities after one year of the pandemic restrictions found that most respondents did not observe a significant impact, and software startups and established companies were affected differently.
Hosna Hooshyar, E. Guerra, Jorge Melegati et al.· IEEE Access· 4 citations
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
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
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
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
