arXiv subjects

Charaka Geethal Kapugama

Publications and source records attributed to Charaka Geethal Kapugama.

2 recordsLinked to original sources

arXiv2026

Extending Delta Debugging Minimization for Spectrum-Based Fault Localization

This paper introduces DDMIN-LOC, a technique that combines Delta Debugging Minimization (DDMIN) with Spectrum-Based Fault Localization (SBFL). It can be applied to programs taking string inputs, even when only a single failure-inducing input is available. DDMIN is an algorithm that systematically explores the minimal failure-inducing input that exposes a bug, given an initial failing input. However, it does not provide information about the faulty statements responsible for the failure. DDMIN-LOC addresses this limitation by collecting the passing and failing inputs generated during the DDMIN process and computing suspiciousness scores for program statements and predicates using SBFL algorithms. These scores are then combined to rank statements according to their likelihood of being faulty. DDMIN-LOC requires only one failing input of the buggy program, although it can be applied only to programs that take string inputs. DDMIN-LOC was evaluated on 136 programs selected from the QuixBugs and Codeflaws benchmarks using the SBFL algorithms Tarantula, Ochiai, GenProg, Jaccard and DStar. Experimental results show that DDMIN-LOC performs best with Jaccard: in most subjects, fewer than 20% executable lines need to be examined to locate the faulty statements. Moreover, in most subjects, faulty statements are ranked within the top 3 positions in all the generated test suites derived from different failing inputs.

cs.SE↗

arXiv2025

Isolating Noisy Labelled Test Cases in Human-in-the-Loop Oracle Learning

Incorrectly labelled test cases can adversely affect the training process of human-in-the-loop oracle learning tech-niques. This paper introduces ISONOISE, a technique designed to identify such mislabelled test cases introduced during human-in-the-loop oracle learning. This technique can be applied to programs taking numeric inputs. Given a compromised automatic test oracle and its training test suite, ISONOISE first isolates thetest cases suspected of being mislabelled. This task is performed based on the level of disagreement of a test case with respect to the others. An intermediate automatic test oracle is trained based on the slightly disagreeing test cases. Based on the predictions of this intermediate oracle, the test cases suspected of being mislabelled are systematically presented for relabelling. When mislabelled test cases are found, the intermediate test oracle is updated. This process repeats until no mislabelled test case is found in relabelling. ISONOISE was evaluated within the human-in-the-loop oracle learning method used in LEARN2FIX. Experimental results demonstrate that ISONOISE can identify mislabelled test cases introduced by the human in LEARN2FIX with over 67% accuracy, while requiring only a small number of relabelling queries. These findings highlight the potential of ISONOISE to enhance the reliability of human-in-the-loop oracle learning.

cs.SE↗