Interview questions on Software Metrics

In this article, we have covered Interview questions on different Software Metrics along with detailed answers.

1. What are Software Metrics?

Software or Metrics Metrics are the measurements of a certain software's specification or some of its properties. It is a method of measuring software development quantitatively. The basic goal of software metrics is to produce a quantitative and repeatable measurement. Software testing Metrics are the numerical measurements that are used to gauge the efficiency, productivity, and overall quality of the software testing process. By offering trustworthy information about the testing process, software testing metrics aim to increase the efficacy and efficiency of the test procedure and support better decision-making for subsequent testing processes.

2. What are the advantages and disadvantages of Software Metrics?
Advantages:

• Software system design methodologies can be compared and researched.
• Software metrics can be used to compare and analyse the traits of different programming languages.
• Software metrics can be used to create the software quality requirements.
• Software system needs and specifications compliance can be confirmed.
• It is possible to deduce the amount of work required for the creation and design of software systems.
• It is possible to estimate the code's complexity.
• It is possible to decide whether or not to divide a complex module.
• Resource managers can be directed to use their abilities to the maximum.
• It is possible to make design trade-offs and compare the costs of software development and maintenance.
• Software metrics can be used to gauge the effectiveness and progress of various stages of the software development life cycle and provide project managers with feedback.
• Software metrics can be used to allocate resources for testing the code.

Disadvantages:

• Metrics are not always simple to use. In some instances, it is challenging and expensive.
• Verifying the accuracy of historical or empirical data used for verification and justification is challenging.
• Software products can be managed, but software measurements cannot be used to assess the technical staff's performance.
• Software metrics are defined and derived using the available tools and the working environment; there is no set method for doing so.
• On the basis of the predictive models, several uncertain variables are estimated.

3. What is software measurement?
A measurement is an expression of the size, amount, quantity, or dimension of a certain aspect of a product or operation. Software is measured in order to:

• Improve the current process or product's quality.
• Consider the product's or process's potential future features.
• Improve the standard of a procedure or product.
• Control how the project is progressing in relation to the budget and timeline.

4. What are the different categories of Software Metrics?
Metrics can be divided into three groups:

• Product metrics: Product metrics include size, complexity, design features, performance, and level of quality of the product.

• Process Metrics - Process metrics can be used to streamline software development and maintenance. Examples include decreasing the rate of development errors, the pattern of testing defect occurrence, and the time it takes for a fixed action to finish.

• Project Metrics - Project metrics are used to describe a project's characteristics and progress. There are several variables to take into account, including the quantity of software developers, staffing patterns across the software life cycle, cost, scheduling, and productivity.

5. What are the characteristics of Software Metrics?

• Quantitative: Metrics need to be quantifiable in order to be valid. This indicates that metrics can be represented as values.
• Understandable: Metric computation should be simple to understand, and the methodology should be well described.
• Applicability: Metrics should be usable during the early stages of software development.
• Repeatable: The metric values should remain constant over time and be consistent in their nature.
• Economical: Metrics should be calculated in an economical manner.
• Language Independent: Metrics should be independent of language, which means that no programming language should be required for their computation.

6. What are the types of Software Metrics?

• Internal metrics: Properties that are judged more significant to a software developer than the consumers are measured using internal metrics. LOC, or lines of code, is one illustration.

• External metrics: Features like portability, reliability, usefulness, usability, and other attributes that are thought to be more essential to users than to software developers are measured using external metrics.

• Hybrid Metrics: Hybrid metrics are metrics that combine resource, process, and product data. An illustration would be cost per function point metric(FP).

7. Explain the Test Metrics Life Cycle.

• Analysis: It is in charge of both defining and identifying the measures.

• Communicate: It assists the testing team and stakeholders in understanding the value and necessity of metrics. It informs the testing team of the information that must be gathered in order to process the statistic.

• Evaluation: It aids in gathering the required information. Additionally, it determines the metric value and confirms the accuracy of the data that was captured.

• Report: It builds the report and provides a strong resolution. It makes the reports available to the testing teams, developers, and stakeholders.

8. What are Manual Test Metrics?
Quality assurance professionals carry out manual testing in a methodical, step-by-step manner. In automated testing, tests are executed using tools, frameworks, and software for test automation. Both manual and automated testing have benefits and drawbacks. Although manual testing is a time extensive method, it enables testers to handle more challenging situations. Two categories of manual test metrics exist:

1. Base Metrics: To offer base metrics, analysts gather data during the design and implementation of test cases. These metrics are given to lead of testing and project managers in the form of a project status report. Utilizing computed parameters, it is quantified.

• The overall quantity of test instances
• the total quantity of tested cases.

2. Calculated Metrics: Calculated metrics are produced using information from base metrics. The test lead gathers this data and refines it to produce more insightful data for monitoring project advancement at the tester, module, and other levels. It's a crucial component of the SDLC as it enables programmers to make essential software changes.

9. Explain defect seeding.
A method known as "defect seeding" was created to determine how many faults are present in a piece of software. Since it's an offline method, not everyone should utilise it. The method is to inject the application with flaws and then check to see if the flaw is discovered or not. Therefore, let's say we introduce 100 flaws, and we want to get 3 parameters. To start, how many seeded problems were found, how many weren't, and how many additional (unseeded) defects are found. We can forecast how many faults will still be present in the system by employing defect seeding.

10. Discussion of software measurement tools and their use in automating the collection and analysis of metrics?
Software measurement tools are tools that automate the collection and analysis of metrics. These tools can be used to collect data on various aspects of a software system, such as code quality, performance, test coverage, and defects. They can also be used to analyze this data and generate reports that provide insight into the quality and performance of the software system.

Some examples of software measurement tools include:

Code analysis tools: These tools analyze source code to identify potential issues such as code complexity, duplicated code, and security vulnerabilities. Examples include SonarQube, CodeClimate, and Fortify.

Test coverage tools: These tools measure the extent to which test cases cover the code of a software system. Examples include JaCoCo, CodeCov and Istanbul.

Performance monitoring tools: These tools measure the performance of a software system, such as response time and resource usage. Examples include New Relic, AppDynamics, and Prometheus.

Defect tracking tools: These tools track and manage defects found in a software system. Examples include JIRA, Bugzilla, and Microsoft Team Foundation Server.

Using software measurement tools can automate the collection and analysis of metrics and provide insight into the quality and performance of a software system. They can also help to identify areas of improvement and provide data to support decision making. However, it's important to note that software measurement tools should be carefully chosen, configured and used in a context, a tool might not be the best fit for a specific system. Additionally, it's important to understand the limitations of the tool, and the metrics it provides, so that the results can be interpreted correctly and make sense in the context of the project and organization.

11. What is Defect Removal Efficiency?
Defect Removal Efficiency (DRE) is a software metric that measures the effectiveness of a software development process in detecting and removing defects before a software system is released. It is calculated as the percentage of defects that are detected and removed during the development process, as opposed to those that are detected after the system has been released.

DRE is calculated using the following formula:

DRE = (Number of defects found and removed during development) / (Number of defects found and removed during development + Number of defects found after release) x 100%

A high DRE value indicates that a high percentage of defects are being detected and removed during the development process, which means that the system is more likely to be of high quality and less likely to have defects after release.

DRE is an important metric for software development teams as it helps to identify potential issues early in the development process, and allows teams to take corrective actions to improve the quality of their software systems. It also helps to measure the effectiveness of the development process and identify areas for improvement.

DRE can be used in conjunction with other metrics such as defect density, which measures the number of defects per unit of size, and defect injection rate, which measures the number of defects introduced into the system per unit of time. These metrics can be used together to gain a more comprehensive understanding of the quality of a software system and the effectiveness of the development process.

12. List some important and common metrics.

• Defect metrics: Engineers can better comprehend the numerous facets of software quality, including performance, functionality, usability, installation stability, compatibility, and other factors, with the aid of defect metrics.

• Schedule Adherence: Calculating the time gap between a schedule's anticipated and actual execution timings is the main goal of schedule adherence.

• Defect Severity: The developer can gauge the impact of the flaw on the software's quality based on how serious it is.

• Test case efficiency: The ability of test cases to find issues is measured by their test case efficiency.

• Defects finding rate: It is employed to identify the trend of defects over time.

• Defect Fixing Time: Defect repair time is the length of time needed to fix a problem.

• Defect cause: It is used to identify the issue's root cause.

• Test Coverage: It details how many test cases have been assigned to the software. This statistic guarantees that the evaluation is finished entirely. Additionally, it helps with the testing of functionality and the validation of code flow.

• Lines of code (LOC): a measure of the size of a software program, calculated as the number of lines of code in the program.

• Function points (FP): a measure of the functionality provided by a software system, based on the number and complexity of user inputs, outputs, inquiries, and files.

• Cyclomatic complexity: a measure of the complexity of a software program, calculated as the number of independent paths through a program's control flow.

• Defect density: a measure of the number of defects per unit of size, usually measured in defects per thousand lines of code (KLOC).

• Code review metrics: a measure of the quality of code review process, including the number of review comments, the number of defects found, and the number of defects found per hour of review.

• Change rate: a measure of the number of changes made to a software system over a period of time, usually measured in terms of changes per month or per year.

• Defect removal efficiency: a measure of the percentage of defects found and fixed during testing, as a percentage of the total number of defects found.

• Mean time to recovery (MTTR): a measure of how quickly a system can be restored to normal operation after a failure.

• Mean time between failure (MTBF): a measure of the reliability of a system, calculated as the total operating time of a system

13. What is agile process metrics?
Agile process metrics are a set of measurements used to evaluate the performance and progress of an Agile software development process. These metrics can provide insight into the efficiency and effectiveness of the team, and can help identify areas that need improvement. Some common Agile process metrics include:

• Sprint velocity: measures the number of user stories completed per sprint, which can indicate the team's productivity and capacity for work.

• Defect density: measures the number of defects found per lines of code, which can indicate the team's quality of work.

• Lead time: measures the time from when a user story is identified to when it is completed, which can indicate the team's efficiency and responsiveness to customer needs.

• Cycle time: measures the time it takes for a user story to go through the entire development process, from idea to deployment, which can indicate the overall speed of the development process.

• Team size: measures the number of team members participating in the process, which can indicate the team's ability to handle work.

• Feedback loops: measures the number of customer feedback loops, which can indicate the team's ability to incorporate customer feedback into the development process.

• Defects found in testing: measures the number of defects found in testing, which can indicate the team's ability to identify and fix defects before deployment.

Overall, these metrics can provide valuable insight into the performance and progress of an Agile development team, and can be used to identify areas that need improvement.

14. Explain Cyclomatic Complexity.
Cyclomatic complexity is a software metric that measures the complexity of a program's control flow. It was introduced by Thomas J. McCabe in 1976. The metric is based on the concept of control flow graphs, which are graphical representations of the different paths that a program can take through its control flow. The cyclomatic complexity of a program is calculated as the number of linearly independent paths through the program's control flow graph.

The cyclomatic complexity of a program can be used as an indicator of its structural complexity, and can help identify areas of the program that may be difficult to understand, test, or maintain. High cyclomatic complexity values can indicate that a program is complex and may be prone to errors, difficult to maintain and test. Cyclomatic complexity can be used as a metric to identify complex functions, and to identify those functions that have a high risk of having bugs.

There are different ways to calculate the cyclomatic complexity of a program, but one common method is to use the following formula:

Cyclomatic Complexity = E - N + 2P

Where:
E = the number of edges in the control flow graph
N = the number of nodes in the control flow graph
P = the number of connected components in the control flow graph (usually 1 for a single program)

15. Explain Software Quality metrics along with examples.
Software quality metrics are measurements used to evaluate the overall quality of a software system. These metrics are used to assess the characteristics of the software that are important for its intended use, such as its functionality, reliability, maintainability, and usability. Some common software quality metrics include:

• Reliability: a measure of the ability of a software system to perform its intended functions without failure.

• Maintainability: a measure of the ease with which changes can be made to the software system, such as fixing bugs or adding new features.

• Usability: a measure of how easy it is for users to learn and use the software system.

• Functionality: a measure of the degree to which the software system meets its intended requirements and specifications.

• Portability: a measure of how easily the software system can be adapted to different environments and platforms.

• Efficiency: a measure of the resource usage of a software system, such as CPU usage, memory usage, and disk usage.

• Security: a measure of the ability of a software system to protect against unauthorized access, use, disclosure, disruption, modification, or destruction of information.

• Scalability: a measure of the ability of a software system to handle increasing workloads or data volumes.

• Testability: a measure of the ease with which a software system can be tested and the degree to which it can be tested.

• Code review metrics: a measure of the quality of code review process, including the number of review comments, the number of defects found, and the number of defects found per hour of review.

These metrics can be used to evaluate different aspects of software quality and to identify areas that need improvement. Software quality metrics are often used during development to track progress and to identify potential issues, and they can also be used to evaluate the quality of a software system after it has been deployed.

16. What is module weakness?
cross-reference lists generated by compilers and assemblers can be used to determine the amount of data in a program and the strength of a module. The cross-reference lists indicate the line where a variable is declared and used, and variables that are defined but never used are not included in the count. The way in which variables are defined and used is important to evaluate the strength of a module, as weak modules can have adverse effects in testing and maintenance. The organization of data within a module should minimize the number of live variables, and programmers must be aware of the status of multiple variables during the programming process. The more variables a programmer must keep track of when constructing a statement, the more difficult it is to construct and maintain. The number of live variables for each statement is also a factor in how difficult it is to track all variables during testing and maintenance.

A variable is considered "live" from its first reference to its last reference within a module. It is possible to calculate the average number of live variables (α) by dividing the count of live variables by the count of executable statements within a module. Live variables depend on the order of statements in the source program, rather than the order in which they are encountered during execution. If the average number of live variables (α) is high, it is assumed that the module will be weak and require more effort during testing. To minimize ct, the life of a variable should be minimized. The life of a variable is defined as the number of statements between its declaration and termination. The average life of a variable within a module (β) can be used to evaluate the weakness of that module. If the average life of a variable is low, it is assumed that the number of live variables will be low, and less effort will be required during testing and maintenance.

The metric can be specified in a range, and it can indicate if a range of testing strategies are feasible for a given module. If none are feasible, it is suggested that the module's testability is low and it should be thoroughly reviewed. The effort needed for testing is an important attribute of a module and it is referred to as testability.

Weakness of a module (WM) = α * β
Where α : average number of live variables
β : average life of a variable

17. What is Functional Point (FP) Analysis?
Functional Point (FP) Analysis is a method used to measure the functionality of a software system. It is a quantitative measure of the functionality of a software system based on the number of functions it provides to its users. The FP method is used to estimate the size and complexity of a software system, and it is particularly useful for software projects that involve multiple languages and platforms.

FP analysis is based on the International Function Point Users Group (IFPUG) standard, which defines a set of functional user requirements that are used to measure the functionality of a software system. The IFPUG standard defines five types of functional user requirements: inputs, outputs, inquiries, files, and interfaces.

FP analysis involves counting the number of functional user requirements in a software system and then applying a set of weighting factors to each requirement to determine its relative complexity. The final result is the number of functional points, which can be used to estimate the size and complexity of the software system.

FP analysis is a useful tool for software project management, as it provides a standardized method for measuring the functionality of a software system. It can be used to estimate the size and complexity of a software system, which can be used to help plan and budget a software project. It can also be used to track the progress of a software project and to identify potential issues or areas that need improvement.

FP analysis is widely used in the software industry, and it is supported by the International Function Point Users Group (IFPUG). Organizations can be appraised against the IFPUG standard by authorized appraisal teams, and they can obtain an official rating of their functional size.

18. Can you explain the concept of mean time to recovery (MTTR) and mean time between failure (MTBF) and their importance in software reliability?
Mean Time to Recovery (MTTR) and Mean Time Between Failure (MTBF) are two important metrics used to measure the reliability of software systems.

Mean Time to Recovery (MTTR) is a metric that measures the average time it takes to recover from a failure. It is calculated by measuring the total time taken to recover from all failures and dividing it by the number of failures. A lower MTTR value indicates that a system is more reliable as it takes less time to recover from failures.

Mean Time Between Failure (MTBF) is a metric that measures the average time between failures. It is calculated by measuring the total time between all failures and dividing it by the number of failures. A higher MTBF value indicates that a system is more reliable as it experiences fewer failures over time.

Both MTTR and MTBF are important in software reliability because they provide a way to measure how well a system recovers from failures and how often it experiences failures. By monitoring these metrics, organizations can identify areas where improvements can be made to increase the reliability of their software systems.

For example, a high MTTR value may indicate that the recovery process from failures is slow and improvements are needed in the recovery process. A low MTBF value may indicate that the software system experiences too many failures, and improvements are needed in the design and implementation of the system to reduce the number of failures.

In summary, MTTR and MTBF are important metrics to evaluate the reliability of software systems. They allow organizations to measure the time it takes to recover from failures and the time between failures, which can help identify areas for improvement to increase the reliability of their software systems.

19. Explain Normalization with respect to software metrics.
Normalization is a process used to adjust software metrics to account for variations in the size or complexity of a software system. Normalization is used to make metrics comparable across different systems or teams, so that they can be used to identify trends or patterns that may indicate areas for improvement.

There are several normalization approaches that can be used with respect to software metrics. Some of these include:

• Size normalization: This approach normalizes metrics based on the size of the software system. This can be done by using metrics such as lines of code, function points, or other measures of the size of the system.

• Functionality normalization: This approach normalizes metrics based on the functionality of the software system. This can be done by using metrics such as function points, which measure the number of functions provided by the system.

• Time normalization: This approach normalizes metrics based on the amount of time that has elapsed since the system was developed or last modified. This can be done by using metrics such as age of the system, which measures the amount of time that has elapsed since the system was developed or last modified.

• Effort normalization: This approach normalizes metrics based on the effort required to develop or maintain a software system. This can be done by using metrics such as developer effort, which measures the amount of effort required to develop or maintain a software system.

• Process normalization: This approach normalizes metrics based on the process followed to develop or maintain a software system. This can be done by using metrics such as process maturity, which measures the maturity of the process followed to develop or maintain a software system.

Normalization is an important process to take into account when using software metrics, as it helps to make metrics comparable across different systems or teams, and identify trends or patterns that may indicate areas for improvement.

20. What are the future trends in software metrics and how they can be used to support software development practices such as Agile and DevOps?
The field of software metrics is constantly evolving, and there are several trends that are likely to shape the future of software metrics and how they are used to support software development practices. Some of these trends include:

Automation: Automation is becoming increasingly important in software development, and there is a growing demand for metrics that can be automatically collected and analyzed. This will make it easier for teams to monitor and track the performance of their software systems in real-time.

Cloud-based metrics: With the increasing adoption of cloud computing, there is a growing need for metrics that can be used to monitor and optimize the performance of cloud-based systems. This includes metrics that can be used to monitor the performance of cloud-based applications, as well as metrics that can be used to optimize the use of cloud-based resources such as storage and compute power.

Predictive analytics: Predictive analytics are becoming increasingly important in software development, and there is a growing demand for metrics that can be used to predict future trends and patterns in software performance. This will help teams to anticipate problems and to take proactive measures to prevent them from occurring.

Continuous integration and delivery: With the increasing popularity of Agile and DevOps practices, there is a growing need for metrics that can be used to monitor and optimize the performance of continuous integration and delivery pipelines. This includes metrics that can be used to track the performance of automated tests, as well as metrics that can be used to monitor the performance of deployment pipelines.

Code quality metrics: With the growing importance of code quality, there is a growing demand for metrics that can be used to measure the quality of code. This includes metrics that can be used to measure the maintainability, testability, and security of code.

All these trends will allow software development teams to gain deeper insights into their software systems, and make data-driven decisions to improve the performance and quality of their software systems. Additionally, these trends will also enable teams to automate their processes, enabling them to work in a more agile and efficient way.