3 Top Product Metrics that reveal Software Quality
Software testing is of prime importance when it comes to Software Development of any sort. Even the slightest error that goes unnoticed during the testing phase can cause huge amount of business and reputation loss to the organisations. Looking at the catastrophic impact that these unnoticed errors can cause, Software Testing has always been regarded as a crucial stage of Software Development Life Cycle (SDLC) process.
However, there is another aspect of Software Development that is of equal importance. That part is, Software Quality Assurance and it is measured by using certain Software Metrics. While Software Testing focuses on the aspects like, if a particular test case has been tested or not, Software Testing Quality Metric is purely a number game and it gives quantifiable results like, number of errors, number of test cases passed, etc.
In a simple and straightforward language, it is the method that helps in achieving a quantitative value of software features. Various quality aspects like, reliability, performance, security, maintainability and coding can be combined to derive suitable metrics and results as per the requirement of a business. Organisations can opt for either manual or automated tests for the software developed by them and gauge how many tests it can pass or give a positive result for.
Software Quality Metrics can be broadly classified into three categories.
1. Product Metrics
2. Process Metrics
3. Project Metrics
In this post, we are going to shed some light upon the Top 3 Product Metrics that reveal Software quality.
Top 3 Product Metrics
Product Metrics measure the complexity, performance and customer satisfaction aspect of Software Quality. The top three metrics within Product Metrics that we are going to discuss are as follows:
1. Mean time to Failure
2. Defect Density
3. Customer Satisfaction
Let’s look at these metrics in detail now.
I. Mean Time To Failure (MTTF)
As the name suggests, MTTF is the time between failures, that is, when the outcome received is different than the expected outcome of a software, this metric gives us the time duration within which the next wrong output is obtained. For example, a MTTF of 50 would mean that within 50 units of time, one failure is occurring in a software. In order words, if once a failure occurs, the next failure is expected to occur after 50 units of time.
This metric is usually used with the systems which are critical in nature, such as air traffic control systems. It is of extreme importance to know the reliability of a system in such situations and MTTF helps in projecting for how long a software can reasonably perform when it goes live based on testing conducted. For this reason, data gathering can be very expensive to measure MTTF. In addition to this, since the exact occurrence of the failure is a necessary data point, there is no room for inaccurate measurement of time between failures.
In most cases, the number of transactions is used as the denominator while measuring MTTF. This implies that, the higher the value of MTTF for a software, the better it is.
The drawback of this metric is, for software that is to be used for commercial purposes and there is no generic user profile, it is comparatively more difficult to implement.
II. Defect Density
Whenever a Software failure takes place, it happens due to the defects that went unnoticed. These defects materialise into failures and cause losses to businesses across the globe everyday.
Defect Density is nothing but the number of defects found in a software as compared to its size. The size of the software can be expressed as lines of code and this metric is used for majority of the commercial software systems.
Lines of Code (LOC) is the most commonly used metric to measure Defect Density. For example, Defect Density of 10 per thousand LOC (represented as 10 KLOC) means that 10 defects exist in a software for every one thousand lines of code. Whereas, 30 per thousand LOC means that 30 defects exist in a software for every one thousand lines of code. Hence, higher the Defect Density, the poorer is the quality of the Software under test.
Like MTTF, Defect Density is measured for data collected over a period of time and complexity of the code should be considered to derive a conclusion because different parts of the code have different level of complexity.
Defect tracking also constitutes an important part of this metric because it is crucial to track the overall defect rate, which should decrease with every release of the software. The appeal of this metric to the organisations also comes into the picture because commercial software development organisations can gauge the cost of maintenance in the SDLC process and analyse the allocation of resources accordingly. In a nutshell, Defect Density helps in recognising the training needs for various employees of an organisation and allocation of project budget.
This metric helps in tracking and detecting high risk components and assurance can be given if testing done was sufficient before the release of the product. Finally, it can also help in understanding the amount of rework required due to defects detected and potential problem areas of the code. Hence, it is not just a game of numbers, but also an important way to ensure quality and effectiveness of the software.
III. Customer Satisfaction
The end goal of commercial Software is to sell and make maximum number of people use it, that is, the customers. Customers are acquired when they believe that the product purchased was satisfactory and worth their while. Especially in this age of Social Media, when an angry customer can cause havoc on platforms like Twitter, Facebook, Instagram and LinkedIn, it is crucial to weigh and measure Customer Satisfaction accordingly.
Customer Experience has been creating quite a buzz and it simply describes the way a customer perceives a Software based upon the treatment provided by it. This Customer Experience is used to gauge how Customer Satisfaction stands in a Software’s market. A survey is conducted to find out about the customer’s experience.
Another way to measure Customer Satisfaction is by calculating Problems per User-Month (PUM) to measure problems that customers experience while using the product. Lower the PUM score, the higher the level of Customer Satisfaction.
Using all the data that is collected above, there are certain metrics that can be derived and used to attain maximum level of satisfaction for the consumer.
Net Promoter Score (NPS)
NPS is the most popular metric that provides an insight into a customer’s mindset. It helps in understanding what is the willingness of the customer to recommend your Software to other people. The response of customers can be taken as numerical values from 1 to 10.
It helps in understanding how loyal a customer can remain to the product. Even a simple question like, ‘How likely are you to recommend this application to your friends?’ can help you get answers to the questions pertaining to NPS as the customers can be quickly categorised into Promoters, Passives and Detractors.
Customer Effort Score (CES)
This metric provides actionable information about the customer by asking a single question about the ease of use of a particular software. The range can be from ‘Very High Effort’ to ‘Very Low Effort’.
The cumulative feedback is then analysed to determine the average CES for a software.
Customer Satisfaction Score (CSAT)
As the name suggests, it is a direct measure of a customer’s satisfaction level. The final result of the CSAT is in the form of percentage and it can also form an important part of Service Level agreements (SLA) signed between organisations for providing Software as a Service (SaaS) .
The response considered is usually on a five point range of Very Satisfied to Very Dissatisfied. The major issue with this metric is that only reflects a short term experience of the customer and cannot be used to gauge the long term loyalty.
Conclusion
We have only discussed the Product based Metrics associated with Software Quality Assurance. That is just the tip of the iceberg in this ocean of possible software metrics that can help in enhancing the Software Quality.
Amongst the various Product Based Metrics, except MTTF, all of them are easy to implement and relevant for commercial products. It is all about monitoring the right metrics and deriving the best possible outcome for the Software longevity and usability. These metrics might seem cumbersome and time consuming, however, the gains achieved from a proper implementation of these metrics in the form of productivity and profitability can make this process worth spending resources upon.
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