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Unlocking the potential of generative AI in the software development life cycle

CIO Business Intelligence

The traditional software development life cycle (SDLC) is fraught with challenges, particularly requirement gathering, contributing to 40-50% of project failures. These challenges persist because companies still rely on traditional SDLC management methods, which can result in slow, error-prone processes. Result: 70% more efficient.

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Four Phases of Maturing Enterprise Agile Development

Social, Agile and Transformation

Establish the SDLC - As youre team completes iterations successfully, the teams practices will begin to gel into a process. Will you make all projects follow an agile practice, or will you set guidelines on when to use agile vs. other practices? Also, see my Top Ten Thoughts for SCRUM Newbies. product lines? multiple businesses?"

Agile 100
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Safeguarding Ethical Development in ChatGPT and Other LLMs

SecureWorld News

Why should AI get a pass on S (Secure) SDLC methodologies? Despite the active contributions of SDLC methodologies over the past 20 years—such as Waterfall, Agile, V-shaped, Spiral, Big Bang, and others—there remains a lack of security-by-design for integration into AI developments such as ChatGPT, DALL-E, and Google's Bard.

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accessiBe enhances accessFlow with deeper dev team integration

Dataconomy

By integrating into the software development lifecycle (SDLC) , the tool helps organizations align with Web Content Accessibility Guidelines (WCAG) and comply with regulatory requirements. AccessFlow 2.0 Key updates in accessFlow 2.0

SDLC 41
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How Mayhem Is Making AppSec Easy for Small Teams

ForAllSecure

With the guidelines of symbolic execution, Mayhem is able to produce new test cases that are more likely to uncover defects over time as it works its way deeper into new areas of code. Mayhem uses fuzzing along with other techniques to find vulnerabilities in software.

SDLC 40