A Unit Testing Framework for Scientific Legacy Code

Yao, Zhuo; Wang, Dali; Sun, Jianhua; Zhong, Dong

doi:10.1109/csci.2017.163

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…al. [25] propose a recursive compiler-based code analyzer, which analyzes every unit module's and its sub-module's data flow to isolate dependent units to portable independent units to aid in development. Authors propose debuggers with support for single threaded and multi-threaded applications in [26,27].…”

Section: Sprint Development and Testingmentioning

confidence: 99%

Tool assisted Agile approach for Legacy application Migration

Chatterjee,

Majumdar,

Das

et al. 2024

Preprint

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Many organizations rely on legacy applications for critical business pro- cesses, even though it incurs a huge cost due to obsolete hardware, software, compiler and the like. Replacing the business critical legacy requires extensive expertise to understand business requirements, esti- mate and test coupled with high cost. To ensure a strict cost budget of legacy migration projects, migration specialists need assistance to understand the facts of the legacy application component in a short span of time to estimate and deliver the target components. Avail- able program assistance tools do not address the needs to migrate the legacy applications. We have designed an effective framework to extract the run-time behaviour of the legacy application like function point, complexity, memory accesses and the like so as to estimate the scope and requirements of an application. The framework contains support to comprehend existing legacy applications, develop and test the target application with business-rules, non-functional requirements (NFR), test data, etc. Then we have proposed a tool assisted Agile way-of-working principles by augmenting our tool-set to migrate the legacy application quickly and effciently. The target components can also be validated co- existentially by exchanging live data with the existing legacy. Designed toolset is built on an open-source dynamic instrumentation framework and can be plugged to a wide variety of run-time environments.

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Section: Sprint Development and Testingmentioning

confidence: 99%

Tool assisted Agile approach for Legacy application Migration

Chatterjee,

Majumdar,

Das

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…• Yao, Wang, Riccuito, Yuan, and Fang (2019); Yao, Wang, Sun, and Zhong (2017) designed a UTF to generate unit tests. It is intended to make understanding undocumented source code easier and be suitable for large-scale software.…”

Section: Utfs In Computational Sciencementioning

confidence: 99%

Facilitating Test-Driven Development via Domain-Specific Languages in Computational Science Software Engineering

Gundlach,

Jung,

Hasselbring

2023

Preprint

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Research software plays a critical role in computational science. Therefore, the software should be of high quality. Producing such software involves efforts not only in initial development, but also during maintenance. One concept for improving the quality of software and reducing maintenance efforts is Test-Driven Development (TDD), which involves implementing tests, e.g., unit tests, as a specification for development. The effects are high test coverage and better overall software quality, but also additional effort in implementing tests. By separation of concerns, such additional efforts can be facilitated through the use of tailored notations such as domain-specific languages (DSLs). DSLs provide different levels of abstraction, for example, to separate the definition of tests from other concerns in software development. Not only does this incentivize research software engineers (RSEs) to write tests, but it also helps reduce the time required to write tests. The DSL TDD-DSL addresses this to facilitate the implementation of TDD using the Fortran Unit Testing Framework (UTF) pFUnit 4.0. TDD-DSL is focused on unit test definition and source code integration. It is implemented in Python for system under tests (SUTs) written in Fortran 90 or later. To reduce the effort of defining tests, TDD-DSL separates the test definition from the implementation and setup. Especially the latter can cause additional effort in practice, which can be mitigated by using code generators provided with the DSL tooling. The DSL allows describing test conditions as assertions and test cases as collections of assertions. TDD-DSL provides content assist within code editors for the underlying UTF, reducing the effort required in its deployment. It also supports parsing the SUT to expand the content assist with source code information and high-level error messaging for the test description. The content assist includes publicly available variables, types, functions and routines. TDD-DSL further supports the definition of unit values such as SI units to help Scientific Modelers check unit values in the software at runtime. For this purpose, the DSL tooling enables the generation of code templates for new implementations in the source code according to the defined tests. To simplify extending the DSL and improve overall maintainability, the DSL tooling employs the language server protocol (LSP) and also public available tools such as the ANTLR4 grammar. Using LSP also enables a multi-editor support to integrate the DSL tooling into different editors and IDEs. In this paper we report on the design and implementation of the DSL and its tooling as well as the evaluation with Scientific Modelers and RSEs.

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Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects

Wang,

Yu,

Chen

et al. 2024

ACM Trans. Softw. Eng. Methodol.

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Deep Learning (DL) models have rapidly advanced, focusing on achieving high performance through testing model accuracy and robustness. However, it is unclear whether DL projects, as software systems, are tested thoroughly or functionally correct when there is a need to treat and test them like other software systems. Therefore, we empirically study the unit tests in open-source DL projects, analyzing 9,129 projects from GitHub. We find that: 1) unit tested DL projects have positive correlation with the open-source project metrics and have a higher acceptance rate of pull requests, 2) 68% of the sampled DL projects are not unit tested at all, 3) the layer and utilities (utils) of DL models have the most unit tests. Based on these findings and previous research outcomes, we built a mapping taxonomy between unit tests and faults in DL projects. We discuss the implications of our findings for developers and researchers and highlight the need for unit testing in open-source DL projects to ensure their reliability and stability. The study contributes to this community by raising awareness of the importance of unit testing in DL projects and encouraging further research in this area.

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A Unit Testing Framework for Scientific Legacy Code

Cited by 3 publications

References 9 publications

Tool assisted Agile approach for Legacy application Migration

Tool assisted Agile approach for Legacy application Migration

Facilitating Test-Driven Development via Domain-Specific Languages in Computational Science Software Engineering

Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects

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