This article provides insights into the concept of Artificial Intelligence and how we at Q-Pros leverage such an important technology to help us increase our testing coverage and efficiency to provide our clients with higher quality at lower cost.
AI has been steadily growing across multiple industries and business cases. It has not only helped in improving the final service to end-users, it has also helped the software production stakeholders to do better work in less time and effort. The testing function is one of those which got big help in leveraging AI technologies to improve validation coverage while reducing the associated effort and time.
Here at Quality Professionals, our test engineers leverage AI test bots in testing web and mobile applications on behalf of our clients.
Over the last couple of decades, testing methodologies have gone through multiple stages:
- Classic manual testing: long time frame (months to years).
- Agile Testing: less time consumption (weeks to months).
- DevOps Testing: Uninterrupted testing is so-called continuous testing (weeks to days).
- Digital Testing: Performed by AI technologies including machine learning, predictive analysis, cognitive QA, and robotics (days to minutes).
Basics of AI
Let’s start by demystifying some of the terminologies related to AI:
- Artificial Intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.
- Machine Learning (ML) evolved from the study of pattern recognition and computational learning theory (studying design and analysis of ML algorithms) in AI. It is a field of study that gives computers the ability to continuously learn without being explicitly programmed.
- Deep Learning (DL) is one of the many approaches to ML. Other approaches include decision tree learning, inductive logic programming, clustering, and Bayesian networks. It is based on neural networks in the human body. Each neuron keeps learning and interconnects with other neurons to perform different actions based on different responses.
There are 3 types of widely used ML algorithms:
- Supervised Learning – We are giving the right training data (input/output combinations) for the algorithm to learn (e.g. extracting text from audio).
- Un-supervised learning – We give a bunch of data and see what we can find (e.g. taking a single image and creating a 3D model).
- Reinforced learning – Based on the concept of the reward function. Rewarding Good/Bad behavior and making the algorithm learn from it (e.g. training a dog)
How is AI Changing Software Testing?
- AI helps increase the testing coverage, especially for complex business areas, specifically when the companies do not have the time and resources to execute exhaustive testing and detect areas within the application that requires attention.
- Since software testing consumes time, resources, and money, AI can help to speed up software testing as every process is automated as well as minimize manpower costs.
- AI eliminates the guesswork. Software engineers can get an idea of what needs to be changed since it can detect defects in the software application and provide them with complete information on where testing is needed and where there is a need for change.
- Software testing is a repetitive process where each application needs to be evaluated at every phase, and repetitive manual testing consumes a lot of time. AI uses a set of algorithms to analyze software functions and identify errors through automated testing, thus minimizing the headaches of repetitive software testing tasks (such as regression tests), improving accuracy, and accordingly shortening time to market.
- Through machine learning, AI helps to generate test data where testers can feed the data into an AI machine allowing it to perform various tests at every stage without the need for manual testing hence improving the reliability and security of the software.
Benefits of Testing in AI
- Improved accuracy – even the most meticulous tester is bound to make mistakes while carrying out monotonous manual testing. This is where automated testing helps by performing the same steps accurately every time they are executed and never missing out on recording detailed results. Testers freed from repetitive manual tests have more time to create new automated software tests and deal with sophisticated features.
- Going beyond the limitations of manual testing.
- Helps both developers and testers – shared automated tests can be used by developers to catch problems quickly before sending them to QA. Tests can run automatically whenever source code changes are checked in and notify the team or the developer if they fail. Features like these save developers time and increase their confidence.
- Increase in overall test coverage – with automated testing, one can increase the overall depth and scope of tests resulting in overall improvement of software quality. Automated software testing can investigate memory and file contents, internal program states, and data tables in order to determine if the software is behaving as it is expected to. All in all, test automation can execute 1000+ different test cases in every test run providing coverage that is not possible with manual tests.
- Saved Time & Money (Faster time to Market) – with software tests being repeated every time source code is modified, manually repeating those tests can be not only time-consuming but extremely costly. In contrast, once created – automated tests can be executed again and again, with zero additional cost at a much faster pace. Software testing timespan can be reduced from days to mere hours which translates directly into cost savings.