The Key to Quality Software: Understanding 5 Metrics

By NIIT Editorial

Published on 22/06/2023

Metrics for software quality are numerical measurements of the state of a software product. To guarantee the software system delivers as expected, these measurements are monitored regularly. Software systems may be evaluated using a wide variety of software quality measures, each of which gives insight into the system's quality from a somewhat different angle.

The performance, dependability, maintainability, and security of the system, among other things, may all be better understood with the use of these metrics for the development team. Better software systems and happier end-users may be the outcome of employing these indicators to gauge success.

This article discusses 10 key software quality metrics that every programmer should know.

Table Of Contents

Code Coverage
Cyclomatic Complexity
Coupling and Cohesion
Mean Time to Failure (MTTF)
Mean Time to Repair (MTTR)
Conclusion

Code Coverage

Code coverage is a statistic used to evaluate the thoroughness with which a software system's source code has been evaluated. It reveals how much of the programme has been tested thus far. To put it another way, it reveals what percentage of the code has been tested.

1. Importance of Code Coverage in Ensuring Software Quality

Developers need code coverage metrics to understand how well their tests are doing. If the proportion of tested code is large, it's likely that most problems and flaws have been found and fixed.

2. How to Measure Code Coverage

The amount of code that was tested may be determined with the use of a code coverage tool. A report detailing how much of the code was run throughout the tests is generated by the tool.

3. Best Practices for Improving Code Coverage

Developers should strive to create tests that cover as much of the code as feasible in order to increase code coverage. This necessitates creating tests for the code that handle both typical and edge scenarios. Important and complicated bits of code should be tested first by developers. The regularity and predictability with which automated tests are executed also contributes to increased code coverage. Code coverage may be checked and enhanced with the use of CI/CD procedures, which provide constant integration and delivery of code.

Cyclomatic Complexity

The number of possible execution pathways through the code is analysed to determine the cyclomatic complexity of a programme, which is a measure of its complexity. It's a metric for how many choices a programme makes, and it represents the variety of ways one might navigate the code.

1. Importance of Cyclomatic Complexity in Software Development

As a measure of the difficulty with which a programme may be tested and maintained, cyclomatic complexity is crucial. Higher rates of defects and mistakes have been linked to increased cyclomatic complexity, which makes code more difficult to comprehend. Code maintainability and testability may both be enhanced by careful monitoring of cyclomatic complexity.

2. How to Measure Cyclomatic Complexity

Software tools that evaluate the code and tally the program's decision points may be used to determine the program's cyclomatic complexity. Control flow analysis, a graph-based technique, is often used to compute cyclomatic complexity. This method generates a graph representing the program's control flow and counts the total number of distinct ways to traverse it.

3. Best Practices for Improving Cyclomatic Complexity

Developers should strive to reduce the amount of decision points in their code in order to enhance cyclomatic complexity. Refactoring the code to remove unused branches of logic is one way to do this. Modular programming and object-oriented programming are two examples of structured programming approaches that developers may utilise to simplify their code. Furthermore, test-driven development (TDD) may cut down on cyclomatic complexity by inspiring programmers to create tests for every potential use case.

Coupling and Cohesion

While assessing the quality of a software system, it is helpful to look at a few different metrics, such as coupling and cohesion. Coupling quantifies the degree to which modules in a system are dependent on one another, whereas cohesiveness assesses the closeness of relationships between components within a module.

1. Importance of Coupling and Cohesion in Software Design

Metrics like coupling and cohesion are valuable because of the effect they may have on a system's maintainability, dependability, and scalability. Low cohesiveness might make it hard to comprehend the module's purpose and operation, while high coupling can make it hard to make changes to a system without impacting other elements of the system.

Developers may increase their software's modularity, flexibility, and maintainability by measuring and controlling coupling and cohesion.

2. How to Measure Coupling and Cohesion

Data flow analysis, control flow analysis, and dependency analysis are only a few examples of coupling measurement methods. Metrics like LCOM (Lack of Cohesion in Methodologies) and CBO (Common Base of Understanding) may be used to assess cohesion (Coupling Between Objects). Methods, classes, and modules are only some of the system pieces that are analysed by these metrics.

3. Best Practices for Improving Coupling and Cohesion

Developers should work at decreasing the reliance between system modules if they want to enhance coupling. To accomplish this, well-defined interfaces between modules may be created via the use of encapsulation, abstraction, and modularization.

Developers may increase cohesiveness by aiming to organise relevant parts inside a module and making sure that each module has a clear function. Information concealing, cohesion-driven design, and the single responsibility principle are all methods that may help with this. Code modularity and maintainability may both be enhanced via the refactoring process, which also helps with coupling and coherence.

Mean Time to Failure (MTTF)

A measure of a system's or component's or device's dependability is its Mean Time to Failure (MTTF). The mean time before a system fails under typical circumstances of use. Although MTTF is most often used to measure hardware system dependability, it may be used to software as well.

1. Importance of MTTF in Measuring Software Reliability

For evaluating the stability of a software system, MTTF is useful since it indicates how often problems are expected to occur. Developers may take action to make their systems more reliable by pinpointing points of failure using MTTF metrics. In addition, MTTF may be used to assess the effect of modifications on system dependability and to compare the robustness of other software systems or versions.

2. How to Measure MTTF

The lack of historical failure data makes it difficult to calculate MTTF for software systems. One method is to utilise statistical methods to estimate the MTTF based on collected data on the amount of faults or failures that occur during a certain time period. Assumptions regarding the failure rates of individual components may be used in conjunction with simulation or modelling approaches to arrive at an estimate of the MTTF.

3. Best Practices for Improving MTTF

Developers should prioritise increasing system dependability by decreasing defect and failure rates in order to lengthen MTTF. Code reviews, automated testing, and continuous integration and deployment are all methods that may help with this. Also, developers should focus their testing and debugging efforts on the most vulnerable aspects of the system, such as mission-critical features or components with a high defect rate. Lastly, developers should keep an eye out for system failures, analyse the collected data, and utilise that information to make adjustments that will increase the system's dependability.

Mean Time to Repair (MTTR)

Mean Time to Repair (MTTR) is a statistic used to evaluate software quality by determining how long it typically takes to fix an application after a failure or problem has been discovered. The mean time to resolve (MTTR) is an important indicator of software dependability since it shows how fast problems are addressed and fixed.

1. Importance of MTTR in Measuring Software Reliability

Determining the dependability of a software programme requires measuring the mean time to repair (MTTR). A smaller MTTR indicates that problems are being swiftly identified and fixed, which increases software dependability. When the Mean Time to Repair (MTTR) is high, it may indicate that there are serious problems with the programme that must be fixed, which might result in substantial downtime and decreased productivity.

2. How to Measure MTTR

The mean time to repair (MTTR) is calculated by timing how long it takes to fix a software programme once an error has been discovered. The Mean Time to Repair (MTTR) is determined by dividing the overall time to fix the problem by the total number of occurrences. If it took 10 hours to fix a problem that happened twice, the MTTR would be 5 hours (10 hours divided by 2 incidents).

3. Best Practices for Reducing MTTR

Best practises for lowering mean-time-to-repair (MTTR) and increasing software dependability include:

Automating Test Procedures: Early bug detection via automated testing decreases the possibility of catastrophic failure later in production.
Establishing a Tracking System: By putting in place monitoring software, problems may be seen instantly, resulting in less downtime and quicker fixes.
By Using Agile Practises: The time it takes to provide updates and patches may be shortened using agile approaches like continuous integration and continuous delivery, which in turn improves software dependability.
Using Preventative Upkeep: Reducing MTTR requires proactive maintenance like routine updates and patches to resolve issues before they escalate.

Conclusion

It is impossible to exaggerate the significance of software quality measures in the software development process. Without these measurements, software engineers would have no ability to objectively evaluate the success of their projects. The performance, dependability, maintainability, and security of the system, among other things, may all be better understood with the use of these metrics for the development team.

Better software systems and happier end-users may be the outcome of employing these indicators to gauge success. Every software development aspirant should enrol in a software engineering course to know how to assess and enhance these metrics because of their significance in determining a system's quality.