Quality in Focus - Techniques and Tools for Analyzing Software Quality
Disclaimer: The information presented in this article is for informational purposes only and should not be considered professional advice. Readers should seek additional guidance or consult with a professional before making any decisions based on the information provided.
Software quality is a critical factor that can impact the success of software development projects.
Software quality is difficult to be evaluated by just obtaining the number and the severity rank of the defects.
Hence, quantitative method shall be utilized to assess and provide clarity to visualize the quality status to all the stakeholders.
We will explore the following topics:
• Analyzing software quality with metrics and indicators
• Method used to analyse software quality
Analyzing software quality with metrics and indicators
The first step in analyzing software quality is to identify the metrics and indicators that are relevant to the project. There are many different metrics and indicators that can be used to analyze software quality, including code coverage, code complexity, defect density, test coverage, and more.
Each of these metrics provides valuable insights into the software's performance and can help developers identify areas that need improvement.
One essential metric for analyzing software quality is code coverage. Code coverage refers to the percentage of the code that has been executed during testing. High code coverage indicates that the software has been thoroughly tested and that there are fewer untested areas that could contain bugs or defects.
Code coverage can be measured using a range of tools, including code coverage analysis tools and automated testing tools.
Another critical metric for analyzing software quality is code complexity. Code complexity refers to the level of difficulty in understanding and modifying the code. High code complexity can make it challenging to maintain and update the software over time, which can lead to additional bugs and defects. Code complexity can be measured using tools such as static analysis tools, which analyze the code and provide insights into its complexity.
Defect density is another important metric for analyzing software quality. Defect density refers to the number of defects per unit of code. High defect density indicates that there are many bugs or defects in the software, which can impact its reliability and performance. Defect density can be measured using tools such as defect tracking software, which helps developers identify and track bugs and defects in the code.
Test coverage is another critical metric for analyzing software quality. Test coverage refers to the percentage of the code that has been tested. High test coverage indicates that the software has been thoroughly tested and that there are fewer untested areas that could contain bugs or defects. Test coverage can be measured using tools such as test coverage analysis tools and automated testing tools.
Quality Metrics is the information for the visualization of Quality status and the improvement on accuracy of Quality estimation.
Analyzing software quality with metrics and indicators is an essential aspect of software development.
Methods used to analyze software quality
a) Quality Control Chart (QCC) - A chart which visualizes the relation of test progress and defects status.
How to create Quality Control Chart
How to analyze Quality Control Chart
Explanation of Chart :
Analyse progress with QCC
Analyse the quality status with QCC
Scenario of two difference situations
b) ODC Analysis
What is the ODC analysis ?
Why is the ODC analysis necessary？
Example 1: The following graph show the number of defects found based on severity rank.
Note: The program size and test cases are equivalent for both software A & Software B
Question: Which of A and B do you think has better quality?
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After further analysis, the tendency of defect emergence tells you different aspect.
Do you still think Software B has better quality?
Example 2: The following graph shows no significant difference between Software A & Software B.
However, after some analysis and detailed checks it reveal some differences.
Do you still think both Software A & Software B have same quality?
In summary, you shall not just evaluate the quality just by the number or severity rank of defects as you already seen from the previous examples.
ODC is designed for the accurate analysis of software quality.
Before ODC analysis can be performed, a defect management database with detail information is required to be established as illustrated below.
Defect info have to be classified to several areas from various viewpoints.
Each of such viewpoint is called an “ODC attribute”.
There are five attributes in total as shown below.
Beside genuine ODC attributes, general defect info such as “severity”, “date” or “status” are also been used for ODC analysis.
Details of the ODC attributes will be explained below.
Let’s explore what ODC analysis can tell ?
Evaluate Test maturity and potential defects
Evaluate implementation maturity
Evaluate the transition of the severity in order to grasp the defects trend and the overall quality status
Evaluate the quality status by checking on the trend changes at each testing phases
Evaluate the test maturity by the Trigger that detect defects
Evaluate the test maturity by the trend changes at each test phases
Evaluate the maturity of design and implementation by changes on the defect type
The choice of technique or method used for analyzing software quality depends on the specific goals and objectives of the analysis. For example, if the goal is to identify and fix defects in software, then testing techniques may be more appropriate.
In summary, analyzing software quality requires a multidisciplinary approach, involving a range of techniques and methods. The results of such analysis can be used to improve software quality and ensure that it meets the needs of users and stakeholders.
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