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Every project manager in the software development industry is faced with the dilemma of the scope triangle of speed, cost, and quality. At least at one point in time during the development lifecycle, the project teams, especially developers, have to make a software delivery decision. Based on the needs and the requirements of the project, they can either choose to deliver at high speed, at low cost, or at high quality. However, with the increasing pressure on the companies for digital transformation and continuous delivery, timely delivery of the end products and speed in the development and testing process has become a default setting for many organizations. With this, the decision and opportunity cost comes down to cost and quality and finding the optimal balance between both.

Historically, software testing has been an underappreciated phase of the software development cycle, which is slow, resource-intensive, and costly for the projects. This is why it was considered to be the least preferred element of the development phases, and the primary motivation for conducting software testing was the external pressures like customer demands or the industry quality standards, instead of their own conviction for improvement. Even though software testing has gained more traction in the industry and is gradually becoming a requirement for application development, the costs remain high. It is estimated that around 80-90 percent of the total development costs are attributed to manual software testing. According to a survey by Capgemini World Quality Report 2017–2018, many of the senior IT executives from various organizations reported that they have allocated around 32 percent of the total IT budget for software testing for 2020, which is essentially a 26 percent increase. Therefore, given these cost and quality challenges faced by organizations, manual software testing for project teams is akin to eating unpalatable vegetables because they are good for them.

However, with the disruptive automation and artificial intelligence technologies, the testing landscape for the software development industry is rapidly changing. The increased use of automated software testing by independent software testing companies has led to an optimal solution of high speed, high quality, and low costs for the applications.

High Quality at Speed for Software Testing

Software testing is not a simple matter of carrying out a few tests for the application and getting the green light for releasing the product. A thorough software testing approach comprises of intricate and multi-layered processes. It entails addressing the concerns of the end-users, writing appropriate test cases based on user expectations and demands, making sure that the right features and aspects of the applications are covered by test cases, and deciding the right combination of manual and automated testing. For independent software testing companies, testing with greater efficiency comes at the expense of streamlining the testing processes, and proficient resource allocation so that the managers are able to deliver high-quality products on time. In order to cater to these concerns, it is important for the managers to cultivate a testing culture in the company to lay the foundation for the successful implementation of software testing.

The first step in the process, and perhaps the most important one, is to treat quality assurance as to the part of the ongoing process, instead of the last link in the development process. Before the development components are executed, the software testing needs to take place in every iteration in order to achieve the optimal solution. Hence, organizations should embed and integrate software testing and quality assurance process as a regular and ongoing element of the routine development procedure.

The cost and time invested in resolving the issues and bugs grow exponentially as they move up the software development lifecycle. This is why it is of utmost importance for independent software testing companies to integrate the testing in the development process from the initial phases so that all possible bugs are detected early. Through this approach, defects and issues in the system are identified in the planning or early production phases which prevents them from snowballing into much larger and unfathomable problems for developers.

Defect clustering is also an important metric that is used to ensure testing efficiency and high-quality end products. As per to the Pareto principle, around 80 percent of the bugs and defects stem from 20 percent of the modules and components in the system. This means that by identifying the most defect and risk-prone areas in the areas, the testing efforts can be reduced substantially, which consequently positively impacts the speed and quality of the application. The clustering of defects is essentially based on the assumption that if a defect is found in any one aspect of the system, then it is highly likely that there would be other issues and bugs in that aspect as well. Therefore, it is imperative to target most of the testing resources in that area.

The pesticide paradox is also highly applicable in this case. It states that conducting the same set of test cases and scenarios on the application system is not conducive to improving the quality or detecting issues. Instead, this paradox assumes that as soon as reported errors in the system are fixed, the test cases and scenarios become useless. Thus, the project teams should make efforts to continuously review and update the test cases for better adaptability and more test coverage.

Conclusion

A meticulous and robust software testing strategy is necessitated for the quality assurance and development teams to ascertain that they are on the right track, and to ensure the quality of the product. Quality assurance and software testing processes for the projects are integrated into the regular development cycle so that the highest possible quality of software products is ensured while reducing the costs, testing efforts, and time to market. Through increased and effective use of automated and AI-based software testing, project managers and developers are able to implement preventive measures in order to avoid defects and issues in the system altogether.