GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming

• URL: http://arxiv.org/abs/2312.15302v3
• Date: Wed, 5 Jun 2024 07:05:21 GMT
• Title: GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming
• Authors: Jon Ayerdi, Valerio Terragni, Gunel Jahangirova, Aitor Arrieta, Paolo Tonella,
• Abstract summary: GenMorph is a technique to automatically generate Metamorphic Relations (MRs) for Java methods. It uses an evolutionary algorithm to search for effective test oracles that trigger no false alarms and expose software faults in the method under test. Our results show that GenMorph generates effective MRs for 18 out of 23 methods (mutation score >20%), and it can increase Randoop's fault detection capability in 7 out of 23 methods.
• Score: 14.417892080404544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely unexplored problem. This paper presents GenMorph, a technique to automatically generate MRs for Java methods that involve inputs and outputs that are boolean, numerical, or ordered sequences. GenMorph uses an evolutionary algorithm to search for effective test oracles, i.e., oracles that trigger no false alarms and expose software faults in the method under test. The proposed search algorithm is guided by two fitness functions that measure the number of false alarms and the number of missed faults for the generated MRs. Our results show that GenMorph generates effective MRs for 18 out of 23 methods (mutation score >20%). Furthermore, it can increase Randoop's fault detection capability in 7 out of 23 methods, and Evosuite's in 14 out of 23 methods. When compared with AutoMR, a state-of-the-art MR generator, GenMorph also outperformed its fault detection capability in 9 out of 10 methods.

Related papers

• Optimizing Metamorphic Testing: Prioritizing Relations Through Execution Profile Dissimilarity [2.6749261270690434]
  An oracle determines whether the output of a program for executed test cases is correct. For machine learning programs, such an oracle is often unavailable or impractical to apply. Prioritizing MRs enhances fault detection effectiveness and improves testing efficiency.
  arXiv  Detail & Related papers  (2024-11-14T04:14:30Z)
• Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration [54.897493351694195]
  We propose a novel parallel decoding approach, namely textithidden transfer, which decodes multiple successive tokens simultaneously in a single forward pass. In terms of acceleration metrics, we outperform all the single-model acceleration techniques, including Medusa and Self-Speculative decoding.
  arXiv  Detail & Related papers  (2024-04-18T09:17:06Z)
• MeTMaP: Metamorphic Testing for Detecting False Vector Matching Problems in LLM Augmented Generation [15.382745718541063]
  This paper presents MeTMaP, a framework developed to identify false vector matching in LLM-augmented generation systems. MeTMaP is based on the idea that semantically similar texts should match and dissimilar ones should not. Our evaluation of MeTMaP over 203 vector matching configurations, involving 29 embedding models and 7 distance metrics, uncovers significant inaccuracies.
  arXiv  Detail & Related papers  (2024-02-22T12:13:35Z)
• Towards a Complete Metamorphic Testing Pipeline [56.75969180129005]
  Metamorphic Testing (MT) addresses the test oracle problem by examining the relationships between input-output pairs in consecutive executions of the System Under Test (SUT) These relations, known as Metamorphic Relations (MRs), specify the expected output changes resulting from specific input changes. Our research aims to develop methods and tools that assist testers in generating MRs, defining constraints, and providing explainability for MR outcomes.
  arXiv  Detail & Related papers  (2023-09-30T10:49:22Z)
• ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifiers [60.6418431624873]
  Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems. We propose ALGO, a framework that synthesizes Algorithmic programs with LLM-Generated Oracles to guide the generation and verify their correctness. Experiments show that when equipped with ALGO, we achieve an 8x better one-submission pass rate over the Codex model and a 2.6x better one-submission pass rate over CodeT.
  arXiv  Detail & Related papers  (2023-05-24T00:10:15Z)
• MR-Scout: Automated Synthesis of Metamorphic Relations from Existing Test Cases [9.00297842984345]
  We propose MR-Scout to automatically synthesize MRs from test cases in open-source software projects. Over 97% of codified MRs are of high quality for automated test case generation. Our qualitative study shows that 55.76% to 76.92% of codified MRs are easily comprehensible for developers.
  arXiv  Detail & Related papers  (2023-04-15T12:53:32Z)
• Fault-Aware Neural Code Rankers [64.41888054066861]
  We propose fault-aware neural code rankers that can predict the correctness of a sampled program without executing it. Our fault-aware rankers can significantly increase the pass@1 accuracy of various code generation models.
  arXiv  Detail & Related papers  (2022-06-04T22:01:05Z)
• Effective Mutation Rate Adaptation through Group Elite Selection [50.88204196504888]
  This paper introduces the Group Elite Selection of Mutation Rates (GESMR) algorithm. GESMR co-evolves a population of solutions and a population of MRs, such that each MR is assigned to a group of solutions. With the same number of function evaluations and with almost no overhead, GESMR converges faster and to better solutions than previous approaches.
  arXiv  Detail & Related papers  (2022-04-11T01:08:26Z)
• Sampling-Decomposable Generative Adversarial Recommender [84.05894139540048]
  We propose a Sampling-Decomposable Generative Adversarial Recommender (SD-GAR) In the framework, the divergence between some generator and the optimum is compensated by self-normalized importance sampling. We extensively evaluate the proposed algorithm with five real-world recommendation datasets.
  arXiv  Detail & Related papers  (2020-11-02T13:19:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.