May 2005· ACM SIGSOFT Softw. Eng. Notes· Vol 30, pp. 1 - 5· 7 citations· 33 references
Computer ScienceEngineering
TL;DR
This paper presents a high-level view of a measurement framework that has been used with multiple agile software development industrial case studies and proposes that this technology-dependent framework can be used as a strawman for a guideline of data collection, analysis, and reporting of industrial case study research.
The objective of this research is to obtain feedback from industry experts to understand the challenges related to the employability of future-ready software engineering undergraduate students and employs a qualitative study to gather the views of the experts.
Nor Azliana Akmal Jamaludin, Najjah Salwa Abd Razak, U. F. Abdul Rauf et al.· JOIV: International Journal...· 0 citations
A novel holistic theory of requirements engineering (RE) quality is proposed that can serve as a coherent theoretical framework for understanding the success or failure of RE processes and artifacts, and it is envisioned that the theory can serve as a coherent theoretical framework for understanding the success or fail...
Henning Femmer, Julian Frattini· IEEE International Requireme...· 0 citations
This extended article analyzed 213 Brazilian professionals’ perceptions of the benefits and challenges of using DT in software development and compares those collected from other professionals’, serving as a guide for practitioners on the use of DT in software development.
Gabriel Kryvoruchca, R. Parizi, Lauriane Correa et al.· 0 citations
Cross-functional teams are a central organizational model in contemporary software engineering fueled by practices such as Agile development, DevOps and Continuous Integration/Continuous Deployment (CI/CD). Although widely adopted, there is limited empirical support for the effectiveness of these practices in cross-fun...
M. Qasim, Ali Zamil Sharhan Al-Maliki, Murtadha Al-Maliki et al.· 2026 6th International Confe...· 0 citations
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.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.