Software testing metrics are measures of product quality, productivity, and the status of the software development process.
The goal of software testing metrics is to help improve the efficiency and effectiveness of the software testing process. They also provide reliable data about the test process that helps make better decisions for future testing work.
What is Test Metrics?
Metrics is a numeric measure of productivity that reports on the performance of software development. Metrics are usually collected to determine project health by measuring how well the project meets its goals and objectives. Put, metrics provide data about the status of a project. A metric can be anything from the number of bugs found to the number of features implemented.
Why Test Metrics? Why Should You Care?
Software testing metrics are a tool of measurement designed to help you make decisions regarding improving the efficiency and effectiveness of the software testing process. The information gathered from test metrics helps you understand how well your tests perform.
Testing metrics can be used for:
- Identifying areas of concern and determining how to improve software testing
- Report the status of your current test projects, products, and processes.
- Understanding where time is being spent during the testing process
- Track progress against test plans for different builds or releases
- Identify areas of improvement with stakeholders and developers.
- Receive recognition for testing efforts and performance
- Track productivity, efficiency, and effectiveness of the software development process.
Software Test Metrics
Software test metrics are quantitative measures of software development project success. They provide a cost-effective approach to evaluate software and product processes, effectiveness, and efficiency. They also provide reliable data about the test process that helps make better decisions for future testing work.
In other words, we can also mention that software test metrics are software development process parameters that measure the status of software testing. These parameters can be either qualitative or quantitative. Software test metrics can provide a wealth of information about a project by measuring its quality and productivity.
Software metrics are also known as software measurement, although some people distinguish between these terms because they have different definitions. There are several kinds of measurements:
- Quantitative data – any numerical data associated with quality (e.g., defects per KLOC, cost per defect)
- Qualitative data – subjective judgment on testing quality; e.g., “The product feels solid”
- Indicators – measures for which you need to supply your own values, e.g., bugs per month
- Results – measures that are automatically provided by a tool (e.g., code coverage)
Benefits of using software testing metrics
The Software Testing Metrics project gathers all the useful information about software testing metrics. The project aims to help individuals and organizations improve their use of software testing metrics in order to make better decisions for future quality-related activities, like planning, ongoing monitoring, and improvement strategies.
Approaches to Choosing Software Testing Metrics
There are three general approaches to choosing software testing metrics – top-down, bottom-up, and hybrid approach:
- Top Down Approach – estimate the cost for each test case and then sum all these together
- Bottom-Up Approach – estimate total test costs by considering all test case evaluation matrix subscores.
- Hybrid Approach – use a top-down approach for low-value tests and a bottom-up approach for medium to high-value tests.
Software Quality Metric
A measure of quality is used to determine the level or degree of something. Software Quality Metrics tries to quantify some aspects of software product development based on values derived from testing activities.
QA Metric
QA metrics are an important part of the software development process.
The purpose of quality measurement in the testing phase is to support decision-making regarding sufficient quality in a project and whether the product will satisfy its requirements. In defect prevention, tests are conducted with an underlying objective of reducing defects down to zero level as far as possible. Detecting and removing defects from a system through testing guarantees that the final work meets certain standards desired by the customer or organization sponsoring development.
However measured, quality has never been easy to define or measure, yet it remains one of the key objectives for any organization. By analyzing our data intelligently, we can identify trends and patterns that might help us better decide how best to move forward.
Test case metrics
A Test Case Specification(TCS) specifies what tests are to be carried out, in which order, and how many times, even if it has not been developed yet during the test process. We should specify what we can do but not how we will do it. This might include features that have not yet been implemented; for example, an input validation feature may not exist at this point, so we would specify that it must be added before testing begins.
Test plan metrics
Indeed, software development projects are rarely successful without proper planning and management of testing activities. The term master test plan (MTPL), or project test strategy or framework, provides a means by which information about the project can be organized in a single document. It is one of the first documents developed during the test planning phase, which gives an overview of the testing effort.
Testing Effort Metrics
Test effort metrics inform how much effort is devoted to software development and testing. When used properly, these metrics can help you predict project completion dates and help build better test plans.
You can use These four metrics when working with your team and/or client, but many more are available if you need them (you probably won’t). Remember to keep it simple so they’ll read them and try to use these metrics to improve their software testing knowledge.
Metrics in Software Engineering: Definition & Types
There are several different types of metrics available in software testing. We will cover some common ones here – from simple to complex.
Process Metrics in Software Engineering
Test Case Preparation Productivity:
The number of test cases that a tester or team prepares in a unit of time.
Test Design Coverage:
This metric has to do with the depth of test design. A good way to think about it is in terms of fault detection or how much functionality there is to be tested.
The basic idea behind this metric is that you should have a test case for every function in the software system that checks if it works properly.
For example, you might want to test a payroll system’s “Pay” function or the “print pay statement” function. And so, for each of these two functions, you need at least one test case. As a result, if your Test Design Coverage is close to 100%, every software element was tested with a minimum of 1 test case.
On the other hand, if you have few tests that check many functions (for example, 80% coverage), your testing strategy is very poor, and your software might be tested too lightly.
Test Execution Productivity:
This metric concerns the number of test cases executed in a unit of time.
Test Execution Coverage:
This metric has to do with the number of elements that were executed. It is calculated as the ratio between the number of test cases and elements in the system multiplied by 100. For example, if you have 10 test cases and 20 software elements, your coverage amount will be (10 / 20) * 100 = 50%.
Test Cases Passed:
As the name suggests, this metric counts the number of test cases that passed.
Test Cases Failed:
Here is another simple metric – how many test cases failed in a given period? It is important to note which type of failures these are and carry out an ad-hoc investigation with each failure detected. The three main types of failures are as follows:
- Functional failure – a test case that checks whether or not the function works properly and fails. This type of failure is usually the result of errors in software development processes. For example, if you have better code coverage but still many functional failures, it could be due to missing requirements traceability in your test design.
- Technical failure – a test case that is meant to check whether or not the functionality works properly but fails because of an error in the technical environment (for example, due to network issues).
- Business logic failure – a failed test case from a business perspective. This type of failure usually relates to bugs in the product.
Test Cases Blocked:
You might have heard of Test Blockers. It is a test case that fails to execute because of some condition (for example, missing user rights). When such a test case fails, all the following cases are blocked from execution. This metric counts the number of such cases.
It is important to note that many different metrics can be derived from this. For example, the number of test cases blocked because of missing documents (if you use dynamic execution) or the number of test cases blocked in a certain period due to a specific bug.
Product Metrics in Software Engineering
Error Discovery Rate:
This metric represents the number of bugs found, divided by the total number of test cases (equivalently, failed test cases). The higher the ratio, the better.
Defect Fix Rate:
This is the total number of bugs fixed during a given period, divided by the total number of issues found in this period.
Defect Density:
This metric is a bit more complex. To calculate it, you need to find the number of defects per unit of functionality (for example, per function). It is important to note that this value can be presented in different ways:
- Number of bugs/lines of code: If calculated using this method, Defect Density mostly depends on how many bugs per code were found during the bug-hunting session. Moreover, it depends on the type of bug, as some bugs require more lines to describe than others (for example, a NullPointerException can be described in fewer lines than an “if-then” syntax issue).
- Number of bugs/function points: This method calculates the number of defects per unit effort (for example, how many bugs are found in 1000 function points).
- Number of bugs/requirements: This metric is based on the fact that most projects have detailed requirements for all the features they include. It is calculated by dividing the number of bugs by the number of requirements that have been implemented. It is important to note that it can be very stressful if this value is zero or negative since it means you found more defects than features in a given period. However, many believe that “zero” should never appear in this metric; otherwise, it may indicate severe requirements issues or bugs that were not properly found (or fixed).
Defect Leakage:
Defect Leakage is another metric that can give you insights into the efficiency of your development process. It represents the number of defects found in an environment where they should not be present (based on their severity level), divided by the total number of bugs.
For example, if you have Test Coverage measured at 90% and find 10 bugs in production, this metric will be 10%. This value has to be as low as possible.
Defect Removal Efficiency:
This metric represents the ratio of bugs found by testing vs. all the defects that were fixed (or known about).
It is calculated using the formula: Defect Removal Efficiency = the Total number of bugs found during Beta Testing / Total number of defects in Production *100%.
