Code Quality Automation: What’s Around the Corner?
Looking into modern tools and technologies available for code quality automation
Establishing code quality practices is essential for any software development team. However, it’s a time-consuming and repetitive task unless we leverage tools to automate the process.
This article will guide you through different strategies and tools available for modern-day code quality automation.
The Need for Continuous Quality
Code quality isn’t a one-time thing. Therefore, as the code base evolves, we need to continuously assess code quality to ensure that it is in a maintainable state.
If we look deeply into the practices we follow, it’s possible to divide it into two sections.
- Proactive — Establish Code Style Guides to drive developers to comply with the best practices defined for the project and create automated support systems for developers to comply (e.g., Linters).
- Preventive — Creating Quality Gates at different levels so that the code needs to pass through them to end up in a live environment. We can also leverage manual code reviews as well in this regard.
And, we typically start with static code analysis as the first stage of automation.
Static Code Analysis
As the name suggests, static code analysis is simply evaluating the code (without its context) to identify code segments not complying with a given set of rules and guidelines.
It is a technique to examine code and identify software flaws and code compliance before execution.
We typically automate static code analysis in multiple stages, including;
- Developers IDE Level — Using Linters to automate code quality evaluation close to the development.
- Quality Gates at CI — Using the same set of rules (or extended) at the CI environment typically for Pull Requests (Or merge requests) in Git.
We can find many tools that cover most of the programming languages and out there with comprehensive rule sets. A few of the popular tools are as follows;
1. SonarCloud
- The most well-known tool out there, along with its Open Source version.
- A cloud-based code quality and security service tool.
- Many hard-to-find bugs and quality issues are tracked.
- When code errors are identified, it can block the pull request.
2. DeepSource
- Checks for anti-patterns, bug vulnerabilities, performance issues, metrics such as dependency count and documentation coverage are generated and tracked.
- For often occurring issues, the Autofix tool suggests changes and allows you to generate a pull request.
- Provides automated pull requests; the analyzer will scan the updated files and deliver a report to the UI about the issues it detects.
- Integrates well with Bitbucket, GitHub, and GitLab.
3. Embold (Will discuss its AI features in the next section)
- Provides AI-assisted code testing, identifies flaws in code and vulnerabilities and recommends solutions.
- Real-time feedback on the code while being edited.
- Users can utilize the score function to examine the overall quality of the code and identify areas for improvement.
- Quality thresholds can be set using the Quality Gates feature.
And, fast-forward to today, some of the tools push the boundary of static code analysis by leveraging AI and Machine Learning.
AI-Powered Automation: The Future
If you look at the coding mistakes we make, most of them are repetitive. However, the challenge is detecting them using static code analysis since some of them are contextual and need classification or continuous learning.
That’s where AI-Powered automation fills the void.
- Using Classification Learning Algorithms — These algorithms can learn patterns in code and estimate the likelihood of a file containing a bug or detect style issues.
- Using Supervised Learning — Machine learning algorithms can predict the code quality output based on historical data, i.e. labelled data.
Using these tools, developers could gain more actionable insights into the code they produce. So let’s see what the latest code quality review tools powered with AI are.
1. Embold
This static code analysis tool shows you exactly where to start troubleshooting with its component-level issue highlighting.
The tool takes historical data and turns it into a source of knowledge for improving software development by turning historical bugs and code errors into a source of knowledge.
Recommendation Engine (beta) learns from previous issues found in a code base and flags significant problems that can solve before the code is committed.
However, Embold is comparatively expensive than other tools.
2. Code Defect AI
Based on historical commits, Code Defect AI uses a machine-learning classifier predicts committed source code files with a higher chance of a bug.
It uses a machine learning approach with random forests, support vector machines(SVM), multilayer perception(MLP), and logistic regression techniques.
The platform extracts, processes, and labels historical data to train the algorithm and develop a reliable decision model.
Developers can utilize Code Defect AI’s confidence score to determine whether their code is compliant or flawed.
This tool is available on GitHub as an open-source tool.
3. AI Reviewer
Based on SOLID Principles, this intelligent code analysis and measurement tool can perform fully automated code reviews for C++ projects.
AI Reviewer also can discover and report a wide range of software defects and anti-patterns automatically.
Free evaluation licenses and paid commercial licenses are available for AI Reviewer.
4. DeepCode
An AI platform can build recommendations that can implement on its own. It uses millions of open source code commits to learn and can detect issues that people would overlook.
DeepCode figures out what’s logically wrong in the code and how others have solved similar problems.
DeepCode is freely available as open-source software.
Amazon CodeGuru Reviewer
With machine learning-powered recommendations, you can automate code reviews and improve application performance with CodeGuru.
We can integrate CodeGuru and automate code reviews throughout the development process. It gives advice and visual indications for security vulnerabilities, application performance and hard-to-find errors, making recommendations to improve code quality.
AWS provide a free tier version up to 90 days with 100k lines of code for CodeGuru Reviewer.
And, another segment of code quality automation tools is there specializing in different dimensions of code quality. For example, some tools focus DevSecOps. So, let’s look at how these tools help to ensure code quality and automate the process.
DevSecOps for Code Quality Automation
The goal of DevSecOps is to integrate security into the software development workflow. And these tools helps to;
- Detect security issues in the development phase and show actionable items for developers to fix them.
- Monitor and audit the code throughout the development life cycle for traceability.
Similar to code quality automation tools, we can inject these into different stages in the CI pipeline. Some of these tools include;
- Codacy
- SonarCloud (Also has the vulnerability and OWASP assessment inbuilt)
- WhiteSource
And, the good news is, all the tools we have discussed so far helps to reduce the manual and repetitive efforts we need to put for code quality evaluation.
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