The authors also flag a recurring issue: while AI models can often generate syntactically correct code snippets, they frequently lack a semantic understanding of the overall software architecture.

In secure software development, development teams usually write and test their code in dedicated environments before it goes ...

Housing Policy California Enacts Sweeping Exemption to Development-Killing Environmental Law Plus: Real rent decreases in New York City, the return of missing middle housing in Virginia, and how ...

Developing control software for cyberphysical systems and industrial automation faces challenges like hardware integration, real-time responsiveness, and high reliability. Traditional environments ...

Abstract Software development methodologies have evolved significantly, transitioning from traditional Waterfall models to more flexible Agile frameworks. However, the role of Quality Assurance (QA) ...

Now today, Cognition is hitting back with Devin 2.0, an updated version of its agent-native software development platform. It’s unclear what foundation model is powering this version.

To facilitate community-based uELM developments employing GPUs, we have created a portable, standalone software environment preconfigured with uELM input datasets, simulation cases, and source code.

DeepLearning.AI founder Andrew Ng recently stated that with the decreasing criteria for software development, the role of deciding what to develop becomes more crucial, making AI product ...

Building great software is a big challenge, and development teams rely on the software development lifecycle (SDLC) to help them succeed. SDLC defined SDLC stands for software development lifecycle, ...