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While a recent study reveals that many developer-written test cases can encode a reusable Metamorphic Relation (MR), over 70% of them directly hard-code the source input and follow-up input in the encoded relation. Such encoded MRs, which do not contain an explicit input transformation to transform the source inputs to corresponding follow-up inputs, cannot be reused with new source inputs to enhance test adequacy. In this paper, we propose MR-Adopt (Automatic Deduction Of inPut Transformation) to automatically deduce the input transformation from the hard-coded source and follow-up inputs, aiming to enable the encoded MRs to be reused with new source inputs. With typically only one pair of source and follow-up inputs available in an MR-encoded test case as the example, we leveraged LLMs to understand the intention of the test case and generate additional examples of source-followup input pairs. This helps to guide the generation of input transformations generalizable to multiple source inputs. Besides, to mitigate the issue that LLMs generate erroneous code, we refine LLM-generated transformations by removing MR- irrelevant code elements with data-flow analysis. Finally, we assess candidate transformations based on encoded output relations and select the best transformation as the result. Evaluation results show that MR-Adopt can generate input transformations applicable to all experimental source inputs for 72.00% of encoded MRs, which is 33.33% more than using vanilla GPT-3.5. By incorporating MR- Adopt-generated input transformations, encoded MR-based test cases can effectively enhance the test adequacy, increasing the line coverage and mutation score by 10.62% and 18.91%, respectively., Comment: This paper is accepted to ASE 2024

Metamorphic Testing (MT) alleviates the oracle problem by defining oracles based on metamorphic relations (MRs), that govern multiple related inputs and their outputs. However, designing MRs is challenging, as it requires domain-specific knowledge. This hinders the widespread adoption of MT. We observe that developer-written test cases can embed domain knowledge that encodes MRs. Such encoded MRs could be synthesized for testing not only their original programs but also other programs that share similar functionalities. In this paper, we propose MR-Scout to automatically synthesize MRs from test cases in open-source software (OSS) projects. MR-Scout first discovers MR-encoded test cases (MTCs), and then synthesizes the encoded MRs into parameterized methods (called codified MRs), and filters out MRs that demonstrate poor quality for new test case generation. MR-Scout discovered over 11,000 MTCs from 701 OSS projects. Experimental results show that over 97% of codified MRs are of high quality for automated test case generation, demonstrating the practical applicability of MR-Scout. Furthermore, codified-MRs-based tests effectively enhance the test adequacy of programs with developer-written tests, leading to 13.52% and 9.42% increases in line coverage and mutation score, respectively. Our qualitative study shows that 55.76% to 76.92% of codified MRs are easily comprehensible for developers., Comment: accepted by TOSEM'24, and available at https://dl.acm.org/doi/abs/10.1145/3656340

Open source software (OSS) vulnerabilities threaten the security of software systems that use OSS. Vulnerability databases provide valuable information (e.g., vulnerable version and patch) to mitigate OSS vulnerabilities. There arises a growing concern about the information quality of vulnerability databases. However, it is unclear what the quality of patches in existing vulnerability databases is; and existing manual or heuristic-based approaches for patch tracking are either too expensive or too specific to apply to all OSS vulnerabilities., Comment: Accepted to the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

As a mixed result of intensive dependency on third-party libraries, flexible mechanism to declare dependencies, and increased number of modules in a project, multiple versions of the same third-party library are directly depended in different modules of a project. Such library version inconsistencies can increase dependency maintenance cost, or even lead to dependency conflicts when modules are inter-dependent. Although automated build tools (e.g., Maven's enforcer plugin) provide partial support to detect library version inconsistencies, they do not provide any support to harmonize inconsistent library versions. We first conduct a survey with 131 Java developers from GitHub to retrieve first-hand information about the root causes, detection methods, reasons for fixing or not fixing, fixing strategies, fixing efforts, and tool expectations on library version inconsistencies. Then, based on the insights from our survey, we propose LibHarmo, an interactive, effort-aware library version harmonization technique, to detect library version inconsistencies, interactively suggest a harmonized version with the least harmonization efforts based on library API usage analysis, and refactor build configuration files. LibHarmo is currently developed for Java Maven projects. Our experimental study on 443 highly-starred Java Maven projects from GitHub indicates that i) LibHarmo identifies 621 library version inconsistencies covering 152 (34.3%) of projects, and ii) the average harmonization efforts are that 1 and 12 library API calls are affected, respectively due to the deleted and changed library APIs in the harmonized version. 5 library version inconsistencies have been confirmed, and 1 of them has been already harmonized by developers.

Third-party libraries are a central building block to develop software systems. However, outdated third-party libraries are commonly used, and developers are usually less aware of the potential risks. Therefore, a quantitative and holistic study on usages, updates and risks of third-party libraries can provide practical insights to improve the ecosystem sustainably. In this paper, we conduct such a study in the Java ecosystem. Specifically, we conduct a library usage analysis (e.g., usage intensity and outdatedness) and a library update analysis (e.g., update intensity and delay) using 806 open-source projects. The two analyses aim to quantify usage and update practices holistically from the perspective of both open-source projects and third-party libraries. Then, we conduct a library risk analysis (e.g., potential risk and developer response) in terms of bugs with 15 popularly-used third-party libraries. This analysis aims to quantify the potential risk of using outdated libraries and the developer response to the risk. Our findings from the three analyses provide practical insights to developers and researchers on problems and potential solutions in maintaining third-party libraries (e.g., smart alerting and automated updating of outdated libraries). To demonstrate the usefulness of our findings, we propose a bug-driven alerting system for assisting developers to make confident decisions in updating third-party library versions. We have released our dataset to foster valuable applications and improve the ecosystem.