通过静态代码分析提升软件质量_AI阅读总结 — 包阅AI

Software and running a business are inseparable. Software is business, and business is software.

To be successful, simply delivering software that meets a business need isn’t enough. Speed to market is as necessary as delivering functionality. Delays result in increased costs. Competitors beating you to market means lost revenue. Impacts on releases directly translate to losses.

Naturally, this forces developers to produce at a continually accelerated pace to deliver on time or even early. Teams are asked to deliver at speed while avoiding software problems such as bugs, technical debt and security vulnerabilities that lead to poor customer experience and threats to a company’s reputation. Juggling all these demands is difficult for teams and often leads developers down an unhappy and unproductive path.

The Current State of Software Quality

To satisfy all these competing directives, development teams rely extensively on automated application testing to determine whether their applications are ready to be pushed to production. Automated testing helps, but it only determines how well the software runs. In traditional quality terms, this is called measuring the application’s “fitness for use” or the degree to which the application meets the requirements for its intended use. Application testing can only identify if an application meets its intended purpose but has no way to determine how well the software was written. Understanding the quality of your code is at the core of determining the quality of the resulting software.

Take technical debt as an example. Cutting corners in code design leads to the buildup of technical debt and increasingly poor performing software. Yes, application performance can be measured with automated testing. However, the true measurement of technical debt is in the accumulating cost of refactoring or reworking the code to eliminate the technical debt. This is fundamentally the reason for calling it “debt.” You’re saving current time and effort, measured in cost, by cutting corners and deferring that cost to a later date. By deferring that work, you’re accumulating debt that has to be repaid later in additional work to undo and repair the poorly written code. Furthermore, accumulating poorly written code in your codebase means it’s more difficult and costly to extend and build new capabilities into your application, further increasing the cost to develop and slowing down innovation.

It’s not enough to only test that your application is functional. Software quality also means ensuring your code’s reusability, extensibility and longevity, which are needed to meet the demands of a high-performing development team.

Ensuring Code Quality With Static Analysis

Behind high-quality software is high-quality code. The same core coding principles remain true regardless of how the code was written, either by humans or AI coding assistants. Code must be easy to read, maintain, understand and change. Code structure and consistency should be robust and secure to ensure the application performs well. Code devoid of issues helps you attain the most value from your software.

But how can you ensure your code is free of problems? The answer is simple: Integrate static code analysis throughout the software development life cycle (SDLC).

The Importance of Static Analysis

Static code analysis evaluates the quality and security of source code without having to execute a program. It analyzes your code to identify issues that lead to bugs, technical debt and security vulnerabilities. By finding these issues in code and guiding developers through resolving these problems, static analysis ensures your software is more stable, less vulnerable to attack and remains easy to modify or extend later.

Static code analysis can also traverse code and simulate how it executes in a program to uncover deeply hidden issues that application testing can’t discover, such as taint analysis. Taint analysis tracks the flow of data through an application to identify potential security vulnerabilities based on the way the data is handled by both your code and external dependent code. Static application security testing (SAST) is another part of static analysis that analyzes source code for security vulnerabilities so you can find and fix them before they become exposed in your application. Secrets detection, an important part of security, finds hard-coded passwords, keys or access tokens in code, and helps you remove them before they are leaked, preventing the risk of exposure to business-sensitive systems and data.

With a static code analysis tool in place, you can be sure you’re delivering stable, secure software that doesn’t begin to fall apart later. When the U.S. government recommends static code analysis as a part of your security posture, as seen in this national cybersecurity report, because the way developers work “is of critical importance to the national interest,” it’s clear that static analysis is necessary.

Ultimately, it’s no longer a question of whether to implement static analysis but how quickly you can do so.

Application Testing and Static Analysis, Better Together

Automated app testing is still critical to ensuring software quality and functionality. However, pairing application testing together with static analysis is a match made in developer heaven. While static analysis focuses on code quality and reduces the number of problems to be found later in the testing stage, application testing ensures that your software actually runs as it was designed. By incorporating both automated testing and static analysis, developers can manage code quality through every stage of the development process, quickly find and fix issues and improve the overall reliability of their software. A combination of both is vital to software development. In fact, a good static analysis tool can even be integrated into your testing tools to track and report the percentage of code covered by your unit tests. Sonar recommends a test code coverage of 80% or your code will fail to pass the recommended standard.

Analyzing projects for the first time with a static code analysis tool can be daunting, especially for larger projects. However, by following a Clean as You Code approach, developers only need to focus on newly written code rather than being overwhelmed by issues in legacy code. By nature of ​​touching legacy code when working on new features, ​​the legacy code will become cleaned over time.

Automated solutions that seamlessly integrate into your existing DevOps tools and workflows, like SonarQube, SonarCloud and SonarLint,empower developers to adopt this Clean as You Code approach with little disruption.

Software and Code Quality Is the Only Path to Success

Developers are in a powerful position to ensure goals are achieved and the needs of their software’s end users are satisfied. But it isn’t enough for the developed software to run if it’s doing so on shaky, unstable legs. By ensuring the quality, security and solidity of an application’s code, companies and development teams can be confident that their software will drive continued success and retain value for years to come.