How can AI help fill quality gaps in software quality assurance?

Software QA & Testing are important functions of software development and are a necessity despite any costs, time, manpower, and financial constraints. Current methods of validating software applications during the development process rely on a combination of manual and automated testing tools. 

At the end of the day, the purpose of Software QA & Testing is to address and remediate the quality gaps that exist in software applications. The two types of quality gaps that can exist in a product are a Producer Gap and a Customer Gap. These are not necessarily restricted to software, but for the purpose of our discussion, we will focus on the quality gaps in software.

  • A Producer Gap is when the delivered software does not meet the internal definitions of the requirements, standards, or expected outcomes. 
  • A Customer Gap occurs when the customer is not able to find satisfaction in using the product for their needs.

AI for software qualityRef: Quality Basics (2017) (University of Connecticut)

If done properly, software testing can help identify the producer gaps very effectively and help the development teams in resolving issues. Many companies have this process optimized with either manual or automated methods to achieve the best outcomes, as there are fairly well-defined rules to be adhered to during testing.

However, addressing customer gaps usually depends on a time consuming and often cumbersome process of discovery by using 

— Customer surveys, 

— Involving users during the Joint Application Development sessions, or 

— Involving users in the testing process with User Acceptance Testing

The above-mentioned tasks work well during the development and pre-deployment process to help deploy the best quality software for customers. 

What the process above does not cover are customer gaps that arise when the users are actually using the product. Once deployed, the software development companies and their teams are best equipped to be as reactive as possible to customer complaints, instead of proactively identifying customer gaps and remediating them.

This is precisely where AI can help fill the gap in the Quality Assurance process of developing software and reduce the pain customers and companies experience when a user faces an issue.

How can AI help Software QA?

We are talking about using AI for much more than automation in testing. Software development teams have automated many testing functions, shortening the testing cycle and delivering real-time results. These fill the need for addressing the Producer gaps in software quality very effectively.  

But how can we identify and mitigate customer gaps with the same precision? Let us first understand what they are in the first place.

Customer gaps are those that make the user feel like they are NOT being served well by using our software. This can be in the form of: 

  • broken functionality, 
  • unclear presentation, which can create confusion for the users, 
  • lack of clear instructions or 
  • In general, a lack of user-friendly interfaces that can promote user engagement and help the user in achieving their goals. 

Note that some of the above is subjective in nature from the perspective of the user and are not easily quantifiable. However, what can be quantified are the user’s actions in the software and whether they achieved their goals.

Using AI, precisely this information, can be collected and analyzed to identify the points of friction and disruption in a user journey and surfaced to the appropriate stakeholders to proactively remediate the situation. 

AI can be applied to the data that is collected as users navigate through the systems and perform different actions in real-time. This allows Product Managers and Customer Success managers, understand the area in the application where users are having issues and provide them with the needed help, contributing to a positive user experience. This will also help the development team to get valuable insights on various user metrics like user stickiness, user-friendliness, user behavior, and glitches/bugs, etc., to drive a better user experience. 

Other benefits of using AI for QA include – 

  • Wider Scope: Manual software QA has its limitations in terms of test cycles and users. AI-driven QA can simulate unlimited scenarios, look deep into memory, files, and data tables to detect the smallest of bugs.
  • Real-time Testing: AI can act like the silent observer watching as users go through various scenarios and capture the results at micro-levels and in real-time. Developers also have a higher level of visibility into how their code is performing and can validate their code not only in the lower environments but also in production. 
  • Faster Testing: Manual testing is not only expensive but time-consuming. AI-driven software quality analysis is continuously running, at no additional costs, and provides immediate results in real-time. This allows businesses to not only optimize their testing process but also have an invisible arm gathering real-time user data.
  • Self-Learning: Each successive data point that is gathered and analyzed by AI only helps to improve the outcomes in the future if validated correctly. Over time, a self-learning algorithm to validate software quality would evolve, requiring a lot less human involvement while improving the productivity and quality of software development teams.

Our product, AIMS Software Quality Intelligence, can help you track all essential QA metrics, monitor user activity and experience in a simple and cost-effective way. Our platform can deliver real-time insights to help your team quickly identify and fix various issues in your software. For a free demo or to know more about AIMS Software Quality Intelligence, contact us here.