Software Metrics Examples
What is Software Testing Metric?
- Software Testing Metrics Examples
- Software Testing Metrics Examples
- Software Metrics
- Software Quality Metrics Examples
Software Testing Metric is be defined as a quantitative measure that helps to estimate the progress, quality, and health of a software testing effort. A Metric defines in quantitative terms the degree to which a system, system component, or process possesses a given attribute.
A software metric is a measure of software characteristics which are quantifiable or countable. Software metrics are important for many reasons, including measuring software performance, planning work items, measuring productivity, and many other uses. The lines of source code in a software component is a commonly used approximation of software complexity. This has a wide variety of uses such as measuring software development productivity with a metric such as lines of code per day per developer.
The ideal example to understand metrics would be a weekly mileage of a car compared to its ideal mileage recommended by the manufacturer.
Software testing metrics - Improves the efficiency and effectiveness of a software testing process.
Software testing metrics or software test measurement is the quantitative indication of extent, capacity, dimension, amount or size of some attribute of a process or product.
Example for software test measurement: Total number of defects
In this tutorial, you will learn-
Why Test Metrics are Important?
- Take decision for next phase of activities
- Evidence of the claim or prediction
- Understand the type of improvement required
- Take decision or process or technology change
Read more about its Importance of Test Metrics
Types of Test Metrics
- Process Metrics: It can be used to improve the process efficiency of the SDLC ( Software Development Life Cycle)
- Product Metrics: It deals with the quality of the software product
- Project Metrics: It can be used to measure the efficiency of a project team or any testing tools being used by the team members
Identification of correct testing metrics is very important. Few things need to be considered before identifying the test metrics
- Fix the target audience for the metric preparation
- Define the goal for metrics
- Introduce all the relevant metrics based on project needs
- Analyze the cost benefits aspect of each metrics and the project lifestyle phase in which it results in the maximum output
Manual Test Metrics
In Software Engineering, Manual test metrics are classified into two classes
Page: 418. Category: Social Science. I rigoberta menchu book pdf.
- Base Metrics
- Calculated Metrics
Base metrics is the raw data collected by Test Analyst during the test case development and execution (# of test cases executed, # of test cases). While calculated metrics are derived from the data collected in base metrics. Calculated metrics is usually followed by the test manager for test reporting purpose (% Complete, % Test Coverage).
Depending on the project or business model some of the important metrics are
- Test case execution productivity metrics
- Test case preparation productivity metrics
- Defect metrics
- Defects by priority
- Defects by severity
- Defect slippage ratio
Test Metrics Life Cycle
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How to calculate Test Metric
| Sr# | Steps to test metrics | Example |
| 1 | Identify the key software testing processes to be measured |
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| 2 | In this Step, the tester uses the data as a baseline to define the metrics |
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| 3 | Determination of the information to be followed, a frequency of tracking and the person responsible |
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| 4 | Effective calculation, management, and interpretation of the defined metrics |
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| 5 | Identify the areas of improvement depending on the interpretation of defined metrics |
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Example of Test Metric
To understand how to calculate the test metrics, we will see an example of a percentage test case executed.
Free dbf converter. To obtain the execution status of the test cases in percentage, we use the formula.
Likewise, you can calculate for other parameters like test cases not executed, test cases passed, test cases failed, test cases blocked, etc.
Test Metrics Glossary
- Rework Effort Ratio = (Actual rework efforts spent in that phase/ total actual efforts spent in that phase) X 100
- Requirement Creep = ( Total number of requirements added/No of initial requirements)X100
- Schedule Variance = ( Actual efforts – estimated efforts ) / Estimated Efforts) X 100
- Cost of finding a defect in testing = ( Total effort spent on testing/ defects found in testing)
- Schedule slippage = (Actual end date – Estimated end date) / (Planned End Date – Planned Start Date) X 100
- Passed Test Cases Percentage = (Number of Passed Tests/Total number of tests executed) X 100
- Failed Test Cases Percentage = (Number of Failed Tests/Total number of tests executed) X 100
- Blocked Test Cases Percentage = (Number of Blocked Tests/Total number of tests executed) X 100
- Fixed Defects Percentage = (Defects Fixed/Defects Reported) X 100
- Accepted Defects Percentage = (Defects Accepted as Valid by Dev Team /Total Defects Reported) X 100
- Defects Deferred Percentage = (Defects deferred for future releases /Total Defects Reported) X 100
- Critical Defects Percentage = (Critical Defects / Total Defects Reported) X 100
- Average time for a development team to repair defects = (Total time taken for bugfixes/Number of bugs)
- Number of tests run per time period = Number of tests run/Total time
- Test design efficiency = Number of tests designed /Total time
- Test review efficiency = Number of tests reviewed /Total time
- Bug find rote or Number of defects per test hour = Total number of defects/Total number of test hours
Software Testing Metrics Examples
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A software metric is a standard of measure of a degree to which a software system or process possesses some property. Even if a metric is not a measurement (metrics are functions, while measurements are the numbers obtained by the application of metrics), often the two terms are used as synonyms. Since quantitative measurements are essential in all sciences, there is a continuous effort by computer science practitioners and theoreticians to bring similar approaches to software development. The goal is obtaining objective, reproducible and quantifiable measurements, which may have numerous valuable applications in schedule and budget planning, cost estimation, quality assurance, testing, software debugging, software performance optimization, and optimal personnel task assignments.
Common software measurements[edit]
Common software measurements include:
- Bugs per line of code
- Comment density[1]
- Cyclomatic complexity (McCabe's complexity)
- Defect density - defects found in a component
- Defect potential - expected number of defects in a particular component
- Defect removal rate
- DSQI (design structure quality index)
- Function Points and Automated Function Points, an Object Management Group standard[2]
Limitations[edit]
As software development is a complex process, with high variance on both methodologies and objectives, it is difficult to define or measure software qualities and quantities and to determine a valid and concurrent measurement metric, especially when making such a prediction prior to the detail design. Another source of difficulty and debate is in determining which metrics matter, and what they mean.[3][4]The practical utility of software measurements has therefore been limited to the following domains:
A specific measurement may target one or more of the above aspects, or the balance between them, for example as an indicator of team motivation or project performance.
Software Testing Metrics Examples
Acceptance and public opinion[edit]
Some software development practitioners point out that simplistic measurements can cause more harm than good.[5] Others have noted that metrics have become an integral part of the software development process.[3]Impact of measurement on programmer psychology have raised concerns for harmful effects to performance due to stress, performance anxiety, and attempts to cheat the metrics, while others find it to have positive impact on developers value towards their own work, and prevent them being undervalued.Some argue that the definition of many measurement methodologies are imprecise, and consequently it is often unclear how tools for computing them arrive at a particular result,[6] while others argue that imperfect quantification is better than none (“You can’t control what you can't measure.”).[7]Evidence shows that software metrics are being widely used by government agencies, the US military, NASA,[8] IT consultants, academic institutions,[9] and commercial and academic development estimation software.
See also[edit]
References[edit]
- ^'Descriptive Information (DI) Metric Thresholds'. Land Software Engineering Centre. Archived from the original on 6 July 2011. Retrieved 19 October 2010.
- ^'OMG Adopts Automated Function Point Specification'. Omg.org. 2013-01-17. Retrieved 2013-05-19.
- ^ abBinstock, Andrew. 'Integration Watch: Using metrics effectively'. SD Times. BZ Media. Retrieved 19 October 2010.
- ^Kolawa, Adam. 'When, Why, and How: Code Analysis'. The Code Project. Retrieved 19 October 2010.
- ^Kaner, Dr. Cem, Software Engineer Metrics: What do they measure and how do we know?, CiteSeerX10.1.1.1.2542
- ^Lincke, Rüdiger; Lundberg, Jonas; Löwe, Welf (2008), 'Comparing software metrics tools'(PDF), International Symposium on Software Testing and Analysis 2008, pp. 131–142
- ^DeMarco, Tom. Controlling Software Projects: Management, Measurement and Estimation. ISBN0-13-171711-1.
- ^'NASA Metrics Planning and Reporting Working Group (MPARWG)'. Earthdata.nasa.gov. Archived from the original on 2011-10-22. Retrieved 2013-05-19.
- ^'USC Center for Systems and Software Engineering'. Sunset.usc.edu. Retrieved 2013-05-19.