How Do Metrics and KPIs Help Businesses Measure Success in Software Product Engineering

Business enterprises make use of digital engineering services for several reasons. These include streamlining operations, enhancing efficiency, and staying competitive in the dynamic digital landscape. Software product engineering allows businesses to automate various tasks and processes, thereby minimizing or eliminating the need for manual intervention. Besides, it enables businesses to craft customized digital engineering solutions unique to their requirements. This methodology also generates scalable software solutions based on the needs of a business.


The software solutions play an important role for businesses. They do so by enabling the latter to handle more data, accommodate more users, and expand into new markets. In a day and age where improved customer experience drives the sale or adoption of software applications, a digital engineering company allows businesses to enhance the customer experience. These are achieved by developing intuitive websites, CRM systems, or mobile apps.

Success in the always-changing world of software product engineering goes beyond simply finishing a project. Instead, it has a close relationship with several quantifiable measurements and Key Performance Indicators (KPIs). These metrics serve as navigational tools, guiding software development teams toward efficient processes, high-quality products, and satisfied customers. In this blog, we delve into the crucial key metrics and KPIs that define success in software product engineering.

 

Key Metrics and KPIs to Measure Success in Software Product Engineering

Metrics are the quantitative metrics employed to rate and confirm the overall effectiveness of software products throughout their lifespan. These enable businesses to understand numerous facets and traits of a software product in terms of its calibre, dependability, effectiveness, and efficiency. Businesses may also use these indicators to make data-driven decisions and obtain a better and deeper knowledge of the quality of their products.

1. Quality Assurance Metrics:

a. Defect Density: Through digital QA testing, this metric measures the number of defects identified in a specific development phase. A lower defect density indicates higher product quality.

b. Code Churn: It quantifies the amount of code changed, added, or deleted during development. High code churn might indicate indecision or instability in project requirements.

2. Development Speed and Efficiency Metrics:

a. Lead Time: The time taken from identifying a task to its completion. Shorter lead times suggest efficient workflows and reduced bottlenecks.

b. Cycle Time: It measures the time to complete one full development cycle. Shorter cycles indicate rapid iteration and development.

3. Customer Satisfaction Metrics:

a. Net Promoter Score (NPS): The NPS measures customer satisfaction and loyalty. It quantifies the likelihood of customers recommending your product to others, providing valuable insights into the customer experience.

b. Customer Effort Score (CES): It gauges the ease with which customers can resolve issues or achieve their goals using your product. Lower effort scores indicate a better user experience.

4. Financial Metrics:

a. Customer Lifetime Value (CLTV): It calculates the total revenue a business expects to earn from a customer throughout their entire relationship. It helps in understanding the long-term value of acquiring a customer.

b. Return on Investment (ROI): ROI measures the profitability of a software product by comparing the net profit to the initial investment. A positive ROI indicates a successful product.

5. Scalability and Performance Metrics:

a. Response Time: This metric obtained through digital assurance and testing assesses the time the software takes to respond to user actions. Low response times are critical, especially in applications requiring real-time interactions.

b. Scalability Index: The scalability index, as part of digital assurance services, measures the ability of the software to handle increased load or user volume. A higher scalability index indicates a robust architecture.

 
6. Innovation and Adaptability Metrics:

a. Feature Adoption Rate: It tracks how users adopt new features. High adoption rates suggest that your product meets customer needs effectively.

b. Time to Market: This metric measures the time taken from conceptualization to launch a new product or feature. Rapid time to market is essential to staying ahead of competitors.

Conclusion

Measuring success in software product engineering is a multifaceted activity involving a careful analysis of various metrics and KPIs. By routinely monitoring these metrics, development teams may pinpoint problem areas, raise customer experience, streamline procedures, and promote innovation. Never forget that obtaining data is just half the battle. The better part is to evaluate it and use it to inform decisions. In digital product engineering, it is also necessary to establish a culture of innovation and ongoing improvement.

Why Automate Software Regression Testing?

 

At the core of every digital transformation exercise is a software application, which, in consonance with the hardware, runs the system and achieves the desired objectives. However, the software application needs to be updated periodically to address the challenges of the day such as the advent of new technologies or changing customer preferences, among others. Software regression testing helps to identify and assess if the changes made to the software application have met their objectives and/or the changes have created a new issue. For example, a photo-sharing service decides to introduce video compatibility. However, when the new service/feature is implemented, the photo-sharing service takes a hit.

Regression testing is to find out such unintended change(s) and fix it before the software application is released to the customers. Thus, QA regression testing ensures the software application is updated to meet the market/technical/customer requirements and is free of any issues.

Scenarios to conduct software regression testing exercise

There can be scenarios or situations for the QA specialists to conduct regression testing to bring about stability to the software application. The below-mentioned test cases are some of the scenarios that necessitate the conduct of such testing:

·       Complex integration test cases

·       Functionalities to be frequently used by the users

·       Scenarios showing the working of a software application’s core features

·       Functionalities to have undergone some recent changes

·       Boundary value test cases

·       Samples of successful or failed test cases

Why regression testing in software testing be automated?

When changes are made to a software application prior to its release, regressions (resident glitches in a functionality or feature) can get triggered. These can negatively impact the release schedule of the software application and increase the development cost. However, there is one way to accelerate QA regression testing and meet the delivery turnaround time – automation. In fact, manually looking for the unintended changes in the application can be time-consuming and entails engaging the testers for extended periods of time. And since testing in such cases can be repeatable in nature, automated regression testing can reduce the time and effort of testing. Since regression testing checks the functioning of the existing features of a software application, it is important to have the maximum test coverage courtesy an automated regression testing strategy.

Factors to consider while deciding a regression testing strategy

QA specialists should consider the following factors while contemplating the decision to pursue automation in regression testing.

# Is test automation suitable for the project?

Test automation, including when used in regression testing, is not suitable for every project. For example, short-term and small projects such as developing small mobile apps, do not need automation to test regression as the time to write automated test scripts may exceed the total timeframe of the project. Hence, automation in regression testing should be for projects that are of large or medium scale, comprising several sub-systems such as enterprise applications and multi-user gaming software with many releases.

# When to write the automated test scripts?

It is not possible to write scripts for automated regression testing beforehand for the process depends on the manual test cases that have found bugs in the past consistently. Since such manual test cases are not available at the start, testers should proceed with automation when stable software modules are present.

# Which are the tests to be automated first?

Regression test scripts may be developed based on repeatable test cases wherein bugs were found consistently through manual testing. Such tests cover core functions and exceptions like boundary conditions or negative test cases impacting other functional areas of the application.

# Quantum of regression testing to be automated

For most software applications around 70-90% of manual test cases that have detected bugs consistently can be automated. The rest 30% are the manual cases that have detected bugs inconsistently or showed false outcomes.

Pitfalls to consider while conducting automation of software regression testing

Test automation experts should keep the following pitfalls in mind while devising an automated regression testing strategy:

·       Review and maintain regression test suites to ensure the efficiency, accuracy, and speed of testing

·       Do away with false positives generated by automated regression testing suites by verifying the results with a manual smoke test

·       The complexity of automating any regression testing process should not come in the way of its implementation

Conclusion

Since regression testing is crucial to ascertain the proper functioning of existing or old functionalities when changes are made to the application, it is crucial and should not be overlooked. Through automation, any regression testing company can maximize test coverage using a minimal number of test cases thereby saving time and cost significantly. 

Article Original Source:
https://dev.to/

How is QAOps different from DevOps in Software Testing

 

With quality being considered the prime differentiator for a software product to be accepted in the market, stakeholders (management, developers, testers, and operations) have their task cut out. They not only have to ensure the quality of the product at the time of delivery but also keep it updated even while the product is being used by the end-users. This means pursuing a cycle of continuous improvement and delivery where everyone in the ecosystem needs to be accountable for maintaining the quality of the product. This is where DevOps kicks in with its focus on reducing the time between developing a software product and the business realizing its benefits.

This calls for increased collaboration and communication between teams, namely, development (those who develop and test the product) and IT operations (those who operate the product). In such scheme of things, Continuous Integration (CI) and Continuous Delivery (CD) are the vital pillars. Thus, with DevOps, business enterprises aim at the rapid and consistent delivery of high-quality products. These products, in turn, are expected to offer higher customer satisfaction, thereby guaranteeing the success of the product in the market. And given the popularity of DevOps, a few variants have emerged, with QAOps being one of them. In a QAOps framework, QA (Quality Assurance) is integrated into the CI/CD pipeline instead of existing as a silo-based process divorced from development and operations.

What is QAOps?

Since Quality Assurance (QA) plays a critical role in the development and delivery of a software application, it is logical that QA and Operations collaborate as a business practice. Thus, QAOps focuses on improving the software delivery workflow and making it stable, robust, secure, and faster. In short, it takes the critical aspects of continuous testing in DevOps such as CI and CD, and brings the siloed teams together.

Why QAOps?

Although less popular than DevOps, QAOps is fast emerging as a crucial methodology to pursue in the delivery lifecycle. In fact, in QAOps, the QA team holds a pride of place that a development team holds in the SDLC. The benefits of employing QAOps in DevOps implementation are as follows:

Better quality: With QA being integrated into the delivery workflow, the final product is of higher quality than what it would have been if traditional methodology had been used. As an integral part of the CI/CD workflow where extensive automation is used, faster results are achieved leading to higher customer satisfaction.

Better productivity: With QA getting much more involved in the development lifecycle than before, there are frequent interactions with other teams. This ensures the QA team is valued more in the organization resulting in boosting their confidence and productivity.

Increased skill level: When the QA team works with other teams (development and operations) as part of DevOps software testing, it allows them to expand their horizon and increase their skill level.

Better customer experience: As QAOps incorporates continuous testing, there is a higher degree of quality, stability, and reliability of the product. This ensures superior experiences for the end customers.

What are the differences between DevOps and QAOps?

Although QAOps is a derivative of DevOps, there are a few differences between the two.

Quality at the core: In DevOps testing services, the collaboration is mainly between the development and operations teams broadly ensuring the final product is free of glitches. Here quality or QA is part of the development process and assumes a ‘secondary’ role in the larger scheme of things. In a DevOps testing strategy, the DevOps specialists mainly comprise developers, testers in a secondary role, and members of the operations team. Thus, even though the main thrust is on quality, the QA specialists remain in the background. However, in QAOps, the operations team mainly communicates and collaborates with the QA team to ensure the continuous delivery of products. Here, QA is not subsumed by the development team but remains an equal stakeholder in the entire value chain. The ultimate result of implementing QAOps testing is to continually deliver superior customer experiences.

Cultural shift: The main thrust of DevOps is to ensure the software is deployable at any point in time with new features in place. Here, every stakeholder should act as part of a single team working toward a common goal. DevOps calls for the development and operations team to function as a single unit aimed at delivering business value across the value chain. In QAOps, the main thrust is to ensure the quality of the application in terms of its performance, scalability, functionality, security, and usability, among others. And since the singular focus in QAOps testing is on achieving the quality of the software application along with the speed of continuous delivery, the quality of the application is of the highest standard.

Conclusion

QAOps or Continuous Testing in DevOps seems to be the next logical step in the implementation of DevOps. It ensures the primacy of ‘quality’ in the SDLC and goes a step further than DevOps in ensuring the quality of the application to remain top-notch on a continual basis without compromising the speed of delivery. 

 Article original source:

https://www.softwaretestingmaterial.com/