Dataset Information

White-box software test generation with Microsoft Pex on open source C# projects: A dataset.

ABSTRACT: The paper presents a dataset on software tests generated using the Microsoft Pex (IntelliTest) test generator tool for 10 open source projects. The projects were selected randomly from popular GitHub repositories written in C#. The selected projects contain 7187 methods from which Pex was able to generate tests for 2596 methods totaling 38,618 lines of code. Data collection was performed on a cloud virtual machine. The dataset presents metrics about the attributes of the selected projects (e.g., cyclomatic complexity or number of external method calls) and the test generation (e.g., statement and branch coverage, number of warnings). This data is compared to an automated isolation technique in the paper Automated Isolation for White-box Test Generation [1]. To the best of our knowledge, this is the largest public dataset about the test generation performance of Microsoft Pex on open source projects. The dataset highlights current practical challenges and can be used as a baseline for new test generation techniques.

SUBMITTER: Honfi D

PROVIDER: S-EPMC7347980 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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White-box software test generation with Microsoft Pex on open source C# projects: A dataset.

Honfi Dávid D Micskei Zoltán Z

Data in brief 20200703

The paper presents a dataset on software tests generated using the Microsoft Pex (IntelliTest) test generator tool for 10 open source projects. The projects were selected randomly from popular GitHub repositories written in C#. The selected projects contain 7187 methods from which Pex was able to generate tests for 2596 methods totaling 38,618 lines of code. Data collection was performed on a cloud virtual machine. The dataset presents metrics about the attributes of the selected projects (e.g., ...[more]

PMID: 32671160

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Project description:BackgroundElectronic health records (EHR) are a key tool in managing and storing patients' information. Currently, there are over 50 open source EHR systems available. Functionality and usability are important factors for determining the success of any system. These factors are often a direct reflection of the domain knowledge and developers' motivations. However, few published studies have focused on the characteristics of free and open source software (F/OSS) EHR systems and none to date have discussed the motivation, knowledge background, and demographic characteristics of the developers involved in open source EHR projects.ObjectiveThis study analyzed the characteristics of prevailing F/OSS EHR systems and aimed to provide an understanding of the motivation, knowledge background, and characteristics of the developers.MethodsThis study identified F/OSS EHR projects on SourceForge and other websites from May to July 2014. Projects were classified and characterized by license type, downloads, programming languages, spoken languages, project age, development status, supporting materials, top downloads by country, and whether they were "certified" EHRs. Health care F/OSS developers were also surveyed using an online survey.ResultsAt the time of the assessment, we uncovered 54 open source EHR projects, but only four of them had been successfully certified under the Office of the National Coordinator for Health Information Technology (ONC Health IT) Certification Program. In the majority of cases, the open source EHR software was downloaded by users in the United States (64.07%, 148,666/232,034), underscoring that there is a significant interest in EHR open source applications in the United States. A survey of EHR open source developers was conducted and a total of 103 developers responded to the online questionnaire. The majority of EHR F/OSS developers (65.3%, 66/101) are participating in F/OSS projects as part of a paid activity and only 25.7% (26/101) of EHR F/OSS developers are, or have been, health care providers in their careers. In addition, 45% (45/99) of developers do not work in the health care field.ConclusionThe research presented in this study highlights some challenges that may be hindering the future of health care F/OSS. A minority of developers have been health care professionals, and only 55% (54/99) work in the health care field. This undoubtedly limits the ability of functional design of F/OSS EHR systems from being a competitive advantage over prevailing commercial EHR systems. Open source software seems to be a significant interest to many; however, given that only four F/OSS EHR systems are ONC-certified, this interest is unlikely to yield significant adoption of these systems in the United States. Although the Health Information Technology for Economic and Clinical Health (HITECH) act was responsible for a substantial infusion of capital into the EHR marketplace, the lack of a corporate entity in most F/OSS EHR projects translates to a marginal capacity to market the respective F/OSS system and to navigate certification. This likely has further disadvantaged F/OSS EHR adoption in the United States.

Project description:BackgroundMoving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common. Feasible approaches to prevent and detect defects in EHR configuration, including CDS tools, are needed. In complex software systems, use of test-driven development and automated regression testing promotes reliability. Test-driven development encourages modular, testable design and expanding regression test coverage. Automated regression test suites improve software quality, providing a "safety net" for future software modifications. Each automated acceptance test serves multiple purposes, as requirements (prior to build), acceptance testing (on completion of build), regression testing (once live), and "living" design documentation. Rapid-cycle development or "agile" methods are being successfully applied to CDS development. The agile practice of automated test-driven development is not widely adopted, perhaps because most EHR software code is vendor-developed. However, key CDS advisory configuration design decisions and rules stored in the EHR may prove amenable to automated testing as "executable requirements."ObjectiveWe aimed to establish feasibility of acceptance test-driven development of clinical decision support advisories in a commonly used EHR, using an open source automated acceptance testing framework (FitNesse).MethodsAcceptance tests were initially constructed as spreadsheet tables to facilitate clinical review. Each table specified one aspect of the CDS advisory's expected behavior. Table contents were then imported into a test suite in FitNesse, which queried the EHR database to automate testing. Tests and corresponding CDS configuration were migrated together from the development environment to production, with tests becoming part of the production regression test suite.ResultsWe used test-driven development to construct a new CDS tool advising Emergency Department nurses to perform a swallowing assessment prior to administering oral medication to a patient with suspected stroke. Test tables specified desired behavior for (1) applicable clinical settings, (2) triggering action, (3) rule logic, (4) user interface, and (5) system actions in response to user input. Automated test suite results for the "executable requirements" are shown prior to building the CDS alert, during build, and after successful build.ConclusionsAutomated acceptance test-driven development and continuous regression testing of CDS configuration in a commercial EHR proves feasible with open source software. Automated test-driven development offers one potential contribution to achieving high-reliability EHR configuration. Vetting acceptance tests with clinicians elicits their input on crucial configuration details early during initial CDS design and iteratively during rapid-cycle optimization.

Dataset Information

White-box software test generation with Microsoft Pex on open source C# projects: A dataset.

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White-box software test generation with Microsoft Pex on open source C# projects: A dataset.

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