The prevalence of software systems has become an integral part of modern-day living. Software usage has increased significantly, leading to its growth in both size and complexity. Consequently, software development is becoming a more time-consuming process. In an attempt to accelerate the development cycle, the testing phase is often neglected, leading to the deployment of flawed systems that can have significant implications on the users daily activities. This work presents TestLab, an intelligent automated software testing framework that attempts to gather a set of testing methods and automate them using Artificial Intelligence to allow continuous testing of software systems at multiple levels from different scopes, ranging from developers to end-users. The tool consists of three modules, each serving a distinct purpose. The first two modules aim to identify vulnerabilities from different perspectives, while the third module enhances traditional automated software testing by automatically generating test cases through source code analysis.

Software testing teams analyse and correct thousands of code on a daily basis to ensure the final product is free of errors. However, the on-demand customer expects software to be comprehensive in functionality and delivered with precision and speed. Current software testing procedures are not scalable to meet these needs, nor are they cost- or time-efficient in the digital economy. As products become more complex to create, the code becomes more challenging to test accurately. Manual testing exposes development teams to many challenges--code changes causing errors elsewhere in the product, the considerable length of regression testing cycles, resourcing constraints of hiring skilled software testers to meet demand, and more.

With the software development life-cycle becoming more complex, and the breakneck pace of new product launches, there is no other choice than to make the software testing process smarter, faster and better. New age technologies such as RPA, AI and ML are getting increasingly adopted to accelerate the software development process. The use of Artificial Intelligence in software development is still at a beginning stage. Through the application of reasoning, problem solving and in some cases Machine Learning, AI can be used to support automation, decrease the amount of mundane and tedious tasks in the development and testing phase. The key value proposition of AI is the fact that it can reduce the direct involvement of the developer or tester in multiple routine tasks.

The complexity of Software development life cycle and the reducing delivery period are increasing at an alarming rate. Correspondingly, there is a need to provide instant feedback and evaluation to the development team. On the other hand, when the developer adds new changes to the code, the existing code, which has already gone through testing, might stop working. So, after every addition made to existing code, new tests are to be conducted for that code. Also, the Tester may always face a plethora of problems and waste a lot of time figuring out perfect solutions for all those problems. It is time for the testers to stop working harder and start working smarter.

Machine Learning (ML) and Artificial Intelligence (AI) have caught the attention of the masses. We've come to the point when we explore the ways these innovative technologies can help us achieve our business' digital transformation goals. Adobe's research from last year shows that only 15% of enterprises were using AI, but forecasted this number to double in only 12 months. If they were right this means that today one-third of organizations have implemented it already. Furthermore, the overall revenues from AI are expected to grow from $9.5 billion in 2018 to the impressive $118.6 billion in 2025 and that's only 6 years from now.