Context-Infused Automated Software Test Generation

Abstract

Automated software testing is essential for modern software development, ensuring reliability and efficiency. While search-based techniques have been widely used to enhance test case generation, they often lack adaptability, struggle with oracle automation, and face challenges in balancing multiple test objectives. This thesis expands the scope of search-based test generation by incorporating additional system-under-test context through two complementary approaches: (i) integrating machine learning techniques to improve test case generation, selection, and oracle automation, and (ii) optimizing multi-objective test generation by combining structural coverage with non-coverage-related system factors, such as performance and exception discovery. The research is structured around four key studies, each contributing to different aspects of automated testing. These studies investigate (i) machine learning-based test oracle generation, (ii) the role of search-based techniques in unit test automation, (iii) a systematic mapping of machine learning applications in test generation, and (iv) the optimization of multi-objective test generation strategies. Empirical evaluations are conducted using real-world software repositories and benchmark datasets to assess the effectiveness of the proposed methodologies. Results demonstrate that incorporating machine learning models into search-based strategies improves test case relevance, enhances oracle automation, and optimizes test selection. Additionally, multi-objective optimization enables balancing various testing criteria, leading to more effective and efficient test suites. This thesis contributes to the advancement of automated software testing by expanding search-based test generation to integrate system-specific context through machine learning and multi-objective optimization. The findings provide insights into improving test case generation, refining oracle automation, and addressing key limitations in traditional approaches, with implications for both academia and industry in developing more intelligent and adaptive testing frameworks.