Quality Assurance of Bioinformatics Software: A Case Study of Testing a Biomedical Text Processing Tool Using Metamorphic Testing
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Abstract (arXiv)
Bioinformatics software plays a very important role in making critical decisions within many areas including medicine and health care. However, most of the research is directed towards developing tools, and little time and effort is spent on testing the software to assure its quality. In testing, a test oracle is used to determine whether a test is passed or failed during testing, and unfortunately, for much of bioinformatics software, the exact expected outcomes are not well defined. Thus, the main challenge associated with conducting systematic testing on bioinformatics software is the oracle problem. Metamorphic testing (MT) is a technique used to test programs that face the oracle problem. MT uses metamorphic relations (MRs) to determine whether a test has passed or failed and specifies how the output should change according to a specific change made to the input. In this work, we use MT to test LingPipe, a tool for processing text using computational linguistics, often used in bioinformatics for bio-entity recognition from biomedical literature. First, we identify a set of MRs for testing any bio-entity recognition program. Then we develop a set of test cases that can be used to test LingPipe's bio-entity recognition functionality using these MRs. To evaluate the effectiveness of this testing process, we automatically generate a set of faulty versions of LingPipe. According to our analysis of the experimental results, we observe that our MRs can detect the majority of these faulty versions, which shows the utility of this testing technique for quality assurance of bioinformatics software.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Feb 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 3.
4 papers.
- 2025Metamorphic Testing of Large Language Models for Natural Language Processingcited by 12
- 2025Metamorphic Testing for Fairness Evaluation in Large Language Models: Identifying Intersectional Bias in LLaMA and GPTcited by 4
- 2025Efficient Fairness Testing in Large Language Models: Prioritizing Metamorphic Relations for Bias Detectioncited by 2
- 2021Metamorphic Relation Prioritization for Effective Regression Testingcited by 1
- 2025Metamorphic Testing of Large Language Models for Natural Language Processing3 Nov 2025
- 2025Efficient Fairness Testing in Large Language Models: Prioritizing Metamorphic Relations for Bias Detection9 May 2025
- 2025Metamorphic Testing for Fairness Evaluation in Large Language Models: Identifying Intersectional Bias in LLaMA and GPT4 Apr 2025
- 2021Metamorphic Relation Prioritization for Effective Regression Testing20 Sep 2021