E-Test: E'er-Improving Test Suites
Ketai Qiu, Luca Di Grazia, Leonardo Mariani, Mauro Pezz\`e

TL;DR
E-Test leverages Large Language Models to identify and generate test cases for untested execution scenarios in production, significantly improving test suite coverage and reducing manual effort.
Contribution
The paper introduces E-Test, a novel approach that uses LLMs to automatically identify and generate test cases for untested scenarios in production environments.
Findings
E-Test achieves a maximum F1-score of 0.55, outperforming state-of-the-art methods.
E-Test effectively targets not-yet-tested execution scenarios in large production datasets.
The approach reduces manual effort in maintaining comprehensive test suites.
Abstract
Test suites are inherently imperfect, and testers can always enrich a suite with new test cases that improve its quality and, consequently, the reliability of the target software system. However, finding test cases that explore execution scenarios beyond the scope of an existing suite can be extremely challenging and labor-intensive, particularly when managing large test suites over extended periods. In this paper, we propose E-Test, an approach that reduces the gap between the execution space explored with a test suite and the executions experienced after testing by augmenting the test suite with test cases that explore execution scenarios that emerge in production. E-Test (i) identifies executions that have not yet been tested from large sets of scenarios, such as those monitored during intensive production usage, and (ii) generates new test cases that enhance the test suite. E-Test…
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