Fragment-Based Test Generation For Web Apps
Rahulkrishna Yandrapally, Ali Mesbah

TL;DR
FRAGGEN is a novel model-based test generation approach for web apps that uses page fragmentation to improve state detection, leading to more reliable regression tests and better model accuracy.
Contribution
FRAGGEN introduces a threshold-free state abstraction technique based on page fragmentation, enhancing near-duplicate detection and test oracle reliability in web app testing.
Findings
Detected 123% more near-duplicates than existing methods.
Inferred models with 62% higher precision and 70% higher recall.
Generated test suites with nearly 100% success rate and 98.7% change detection.
Abstract
Automated model-based test generation presents a viable alternative to the costly manual test creation currently employed for regression testing of web apps. However, existing model inference techniques rely on threshold-based whole-page comparison to establish state equivalence, which cannot reliably identify near-duplicate web pages in modern web apps. Consequently, existing techniques produce inadequate models for dynamic web apps, and fragile test oracles, rendering the generated regression test suites ineffective. We propose a model-based test generation technique, FRAGGEN, that eliminates the need for thresholds, by employing a novel state abstraction based on page fragmentation to establish state equivalence. FRAGGEN also uses fine-grained page fragment analysis to diversify state exploration and generate reliable test oracles. Our evaluation shows that FRAGGEN outperforms…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
