Software development
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3 days agoWhy Code Validation is the Next Frontier - DevOps.com
Shared staging environments are inadequate for modern development; isolated, on-demand setups are needed for effective validation.
Contracts are a means of setting preconditions and postconditions on function declarations, and adding assertion statements within functions. The feature is intended to help make C++ code safer and more reliable.
Dependabot sounded the alarm on a large scale. Thousands of repositories automatically received pull requests and warnings, including a high vulnerability score and signals about possible compatibility issues. According to Valsorda, this shows that the tool mainly checks whether a dependency is present, without analyzing whether the vulnerable code is actually accessible within a project.
A global survey of 2,039 Java developers published today finds 63% reporting that dead and unused code adversely affects their team's productivity, with 22% describing the impact of that technical debt as being severe. Conducted by Dimensional Research on behalf of Azul, a provider of a distribution of OpenJDK, the survey also finds that more than half (56%) now deal with a Common Vulnerability and Exposure (CVE) involving Java on a daily or weekly basis.
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This extends to the software development community, which is seeing a near-ubiquitous presence of AI-coding assistants as teams face pressures to generate more output in less time. While the huge spike in efficiencies greatly helps them, these teams too often fail to incorporate adequate safety controls and practices into AI deployments. The resulting risks leave their organizations exposed, and developers will struggle to backtrack in tracing and identifying where - and how - a security gap occurred.
The software industry is collectively hallucinating a familiar fantasy. We visited versions of it in the 2000s with offshoring and again in the 2010s with microservices. Each time, the dream was identical: a silver bullet for developer productivity, a lever managers can pull to make delivery faster, cheaper, and better. Today, that lever is generative AI, and the pitch is seductively simple: If shipping is bottlenecked by writing code, and large language models can write code instantly, then using an LLM means velocity should explode.
The bot, designated MJ Rathbun or crabby rathbun (its GitHub account name), apparently attempted to change Shambaugh's mind by publicly criticizing him in a now-removed blog post that the automated software appears to have generated and posted to its website. We say "apparently" because it's also possible that the human who created the agent wrote the post themselves, or prompted an AI tool to write the post, and made it look like it the bot constructed it on its own.
Hast mentioned that they trust their unit tests and integration tests individually, and all of them together as a whole. They have no end-to-end tests: We achieved this by using good separation of concerns, modularity, abstraction, low coupling, and high cohesion. These mechanisms go hand in hand with TDD and pair programming. The result is a better domain-driven design with high code quality. Previously, they had more HTTP application integration tests that tested the whole app, but they have moved away from this (or just have some happy cases) to more focused tests that have shorter feedback loops, Hast mentioned.