It is a common secret that the cost of a defect rises significantly the later in the process it is discovered. Find and fix problems in the lab is about 80-100 times cheaper and 50 times faster than fixing a problem after the software is released into the market. A NIST study in 2002 reported that software defects cost the US economy around $59 billion annually, showing that more than one third of this cost could be avoided if better software testing was performed. Many software development companies dedicate over 50% of their resources to finding and fixing defects in later stages of the development process.

The same study shows that the cost of fixing one bug found in the production stage of software is 15 hours compared to 5 hours of effort if the same bug was found in the coding stage.

Since software bugs account for this large percentage of the cost and schedule, it is an area where significant improvements can be made to reduce cost and time to market.

“Every hour spent on defect prevention saves three to ten hours of debugging”

Identifying and resolving a defect early in the software development lifecycle is undeniably going to improve the quality but it is even better when defects can be prevented in the first place. Preventing defects from occurring requires a structured disciplined methodology; including gathering and analysing data and conducting root cause analysis, determining and implementing the corrective actions and sharing the lessons learned between projects  to avoid future defects.

By preventing defects from happening, you minimize the number of interruptions and delays caused by the team having to find and fix these errors.

The more defects you prevent, the lower the risk is of defects slipping through your testing procedures and making their way to the end-user. That would require a significant amount of resources to reproduce the defects, remediate them, re-test, and release the updated application. An automated defect prevention process increases velocity, allowing the team to accomplish more within an iteration.



With the above in mind, it is imperative that any Quality Assurance methodology should strive towards preventing defects at the origination stage. If your business depends on developing software, you can benefit from integrating Predictive Analytics into your processes.  This can help integrate and align data (often from disparate sources) throughout the SDLC and testing process, allowing stakeholders to get to the root cause faster, increase response times, and save the company from risking its reputation.

Unlike traditional reporting tools that provide a "rear-view mirror" perspective on what's already happened, advanced analytics applies algorithms, and can predict future outcomes based on historical trends and behaviour.

When companies adopt an analytics-driven culture, they can mature their QA and DevOps programs to respond more agilely to changing business requirements, extract more value from testing and assume less go-live risk.

“Validata Analytics360° is the first end-to-end DevOps solutions from the cloud to enable clients predict delivery, align tests with business risks, control costs and drive operational efficiencies” says Vaios Vaitsis, Founder and CEO of Validata Group.