Cognitive Fault Prediction and Adaptive Testing Framework
Keywords:
Adaptive Software Testing, Software Fault Prediction, Prediction–Testing Integration, AI-Driven Testing Frameworks, Data-Driven Software Reliability, Machine Learning for Fault Detection, Software Quality Assurance, Test-Case Prioritization, Dynamic Test Allocation, Adaptive Test Scheduling, Repository Mining for Software Engineering, Mining Software Repositories (MSR), Software Reliability Engineering, Data-Driven Quality Models, Next-Generation Software Systems, Testing of Large-Scale Systems, Apache HTTP Server Case Study, Real-World Testing Deployment, Intelligent Test Automation, Software Testing Analytics.Abstract
Next-generation systems present novel testing and reliability challenges. Typical software failures associated with such systems are often observed late in the development cycle and are expensive to fix. A promising technique for coping with such problems is the integration of prediction and testing so that adaptive testing schedules can be constructed. The effectiveness of such adaptive testing strategy is enhanced if fault predictions are made under a data-driven approach. This study employs publicly available software repositories to explore the feasibility of using data-mining and machine-learning approaches for software fault prediction and evaluates two adaptive testing strategies. A case study on the Apache HTTP Server, which is one of the most widely used Internet applications, demonstrates the viability of using data-driven models not only for test-case prioritization and scheduling but also for dynamic test allocation during execution.
In an effort to make such prediction/testing integration feasible, an AI-driven framework is proposed to combine fault prediction and adaptive testing. Data-driven models for fault prediction are constructed using repository data from the Apache HTTP Server, and the test-case allocation and scheduling strategy proposed by Baik et al. is applied as a proof of concept. Such an approach enables the use of fault predictions as input to the adaptive strategy, making the prediction/testing integration practical and conducive to real-world deployment.
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