Driving Reliability and Compliance Effortlessly
Code Quality Tools
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Vector Software Quality
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Static Analysis
- Code Quality
Code metrics are essential for understanding the complexity, maintainability, and areas for software improvement. This data-driven approach provides valuable insights that help optimize performance and guide development efforts. Data Intelligence Solutions in combination with static code analysis tools ensure accurate information, improving code quality and successful project outcomes.
What is Code Quality?
Code quality reflects how well code meets critical characteristics expected of high-quality software, such as functional suitability, maintainability, testability, performance efficiency, portability, and security.
Compliance with these characteristics is often measured using tools that map coding guidelines of common standards to these quality attributes. Static analysis tools check compliance, while code quality analysis models prioritize and evaluate these characteristics.
How Do You Ensure Code Quality?
Consistently and continuously monitoring code quality within the build pipeline ensures transparency and accessibility for all project members. This integration keeps the team informed and contributes to maintaining and improving code quality.
Code Review and Code Quality: What is the Difference?
The main difference is that code review is an action, while code quality is a characteristic of the code. Code reviews ensure compliance with software development processes designed to maintain execution quality and repeatability. By following coding standards, these reviews play a crucial role in achieving high code quality and supporting overall software quality.
What is a Code Quality Tool?
Code quality tools assess the code's quality, displaying specific findings and an overall quality rating. They utilize coding standards like MISRA C++ and CERT C and quality models such as SQUARE (System and Software Quality Requirements and Evaluation based on ISO 25000) and SQALE (Software Quality Assessment based on Lifecycle Expectations).
Standards for Software Quality: ISO 25000
The ISO 25000 SQuaRE series offers a framework for evaluating software quality across various dimensions. It includes models for assessing different aspects of quality and guidelines for measuring and managing it.
A Simplified Overview:
Quality Models: These define characteristics like reliability, usability, and maintainability.
Quality Measurement: Guidelines for measuring different aspects of software quality.
Quality Requirements: Processes for defining what quality means for a particular software project.
Quality Evaluation: Methods for assessing software quality against specified requirements.
Differentiating Key Concepts
Quality in Use: This measures the extent to which software meets the needs of specified users and scenarios of use. It is about the user's experience, encompassing effectiveness, efficiency, and satisfaction in a particular context.
Usability: A subset of both quality in use and software quality that focuses on how easily and pleasantly the software interfaces can be used. This is more about the design and interaction elements that contribute to a user-friendly experience.
In essence, Quality in Use evaluates the end-user experience in real-world scenarios, while Usability is more about the attributes of the software that make it easy and enjoyable to use.
The ISO 25000 is also the basis for the so-called “5 pillars of Code Quality”, encompassing the aspects of Readability, Efficiency, Maintainability, Reliability/Resilience and Security. Also, other approaches like SQALE (Software Quality Assessment based on Lifecycle Expectations) use the Quality Model of this ISO standard to measure and manage technical debt.
“Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.”
– H. James Harrington
Code Metrics (HIS)
Metrics offer a quantitative perspective on your code by utilizing various measures such as counts, percentages, sums, averages, and ratios. One notable set of metrics is the HIS-metrics, developed in 2005 by the Manufacturers' Software Initiative of German OEMs. These metrics are widely used to compare and evaluate the quality of software projects.
| Metric | Description | Range |
|---|---|---|
Comment Density “COMF” | Relationship of the number of comments (outside of and within functions) to the number of statements | >0.2 |
Number of paths “PATH” | Number of non-cyclic remark paths (i.e. minimum number of necessary cases of test) | 1 - 80 |
Cyclomatic Complexity “v(G)” | In accordance with the Cyclomatic Number | 1 - 10 |
Number of called functions “CALLS” | How many different functions does this function call? Calling the same subfunction counts only once. | 0 - 7 |
Beyond HIS-metrics, many other metrics are available to evaluate your code. PC-lint Plus offers a comprehensive set of metrics that can be applied to functions, classes, and files. Users can also define custom metrics to address specific requirements. After analysis, the tool generates detailed reports summarizing metric values and highlights any violations when metrics do not meet predefined criteria, providing valuable insights into code quality.
With our solutions, you can accurately measure HIS-metrics and much more.
Innovative Code Quality Dashboard
Explore Squore: Uncover insights into code complexity, maintainability, and improvement opportunities.
Tackling Duplicate Code
While sometimes beneficial, Duplicate code often poses significant maintenance challenges and risks, such as increased effort and potential vulnerabilities due to inconsistent updates and bug fixes. It can also enlarge the codebase and degrade performance. Detecting duplicates extends beyond simple copy-pasting, especially in projects with legacy code or large codebases, requiring deep analysis of algorithms and sequences.
Squore's Source Code Analyzer effectively identifies clones using advanced metrics like the Longest Common Substring and Control Flow Token Cloned, ensuring comprehensive detection of both textual and algorithmic duplicates in extensive codebases.
Benefits:
Real-Time Insights: Always have a clear view of the source code's status and quality.
Trend Analysis: Quickly spot trends, fluctuations, and potential issues by comparing current data with historical performance.
Quality Control: Enforce quality standards with Quality Gates that stop non-compliant code from progressing through the pipeline.
Addressing Technical Debt
Measuring and monitoring your code is essential for managing technical debt. Our tools, PC-lint Plus and Squore identify and resolve existing quality issues and help prevent new debt from accumulating. Despite being a long-standing challenge, technical debt has surged, becoming a significant obstacle to innovation. Tackling it proactively is crucial, and you can take immediate action with our solutions.
Companies that actively manage their technical debt often see a 50% increase in delivery speed, gaining a competitive edge.
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