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2,752 papers
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
In this chapter, the results of a multivocal literature review are presented to offer 118 metrics practitioner experts think software startups should measure that can give you ideas for what your startup should measure.
This investigation revealed different types of AI systems and different AI development approaches, but it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development ofAI systems.
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).
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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.
Software (ISSN: 2674-113X) [...]
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.
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.
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.
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.
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.
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.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
A Blog post by Liquid AI on Hugging Face
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
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.
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