The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.
A Game Theoretic Approach for Quality Assurance in Software Systems Using Antifragility-Based Learning Hooks