Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Code coverage”

PFLOTRAN Code Coverage.

Hammond, Glenn Edward · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2018

Abstract not provided.

Cited by 0Open access

Code Coverage Report

Eyvazov Kanan · ELTE Digital Institutional Repository (EDIT) (Eötvös Loránd University) · 2021

My thesis work is about creating an extension for bitrise.io testing add-on with which the users can see the code coverage of their codebase per commit.

Cited by 0

Code Coverage Reporter

Jacob Curley · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2026

The code-coverage-reporter is a JavaScript GitHub Action that may be called from within GitHub workflows. Its role is to parse a code coverage report file in the LCOV format and translate the contents into a summary that gets added to individual Pull Requests. It is fully reusable and repository-agnostic.

Cited by 0Open access

Code Coverage- Extended Version

Dries Vanoverberghe, Jonathan De Halleux, Nikolai Tillmann et al. · 2011

Currently, testing is still the most important approach to reduce the amount of software defects. Software quality metrics help to prioritize where additional testing is necessary by measuring the quality of the code. Most approaches to estimate whether some unit of code is sufficiently tested are based on code coverage, which measures what code fragments are exercised by the test suite. Unfortunately, code coverage does not measure to what extent the test suite checks the intended functionality. We propose state coverage, a metric that measures the ratio of state updates that are read by assertions with respect to the total number of state updates, and we present efficient algorithms to measure state coverage. Like code coverage, state coverage is simple to understand and we show that it is effective to measure and easy to aggregate. During a preliminary evaluation on several open-source libraries, state coverage helped to identify multiple unchecked properties and detect several bugs. State Coverage: Software validation metrics beyond

Cited by 0

Code Coverage Dataset Sample

­ Anonymous · Zenodo (CERN European Organization for Nuclear Research) · 2022

This is a code coverage dataset sample for submission the The Web Conference 2023. It is submitted anonymously to preserve the double-blind review process.

Cited by 0Open access

Code Coverage and Metrics

Rahul Gopinath · Harvard Dataverse · 2014

This dataset contains source code metrics such as lines of code, cyclomatic complexity number, and code coverage data such as statement coverage, branch coverage, path coverage, condition coverage, loop coverage, data coverage and mutation coverage from tools such as Emma, Cobertura, Codecover, JMockit, and Pittest from a number of projects from Github. The code coverage was collected from original test suites present in the programs, and also generated test suites using Randoop.

Cited by 0Open access

Code coverage at Google

Marko Ivanković, Goran Petrović, René Just et al. · 2019

Code coverage is a measure of the degree to which a test suite exercises a software system. Although coverage is well established in software engineering research, deployment in industry is often inhibited by the perceived usefulness and the computational costs of analyzing coverage at scale. At Google, coverage information is computed for one billion lines of code daily, for seven programming languages. A key aspect of making coverage information actionable is to apply it at the level of changesets and code review. This paper describes Google’s code coverage infrastructure and how the computed code coverage information is visualized and used. It also describes the challenges and solutions for adopting code coverage at scale. To study how code coverage is adopted and perceived by developers, this paper analyzes adoption rates, error rates, and average code coverage ratios over a five-year period, and it reports on 512 responses, received from surveying 3000 developers. Finally, this paper provides concrete suggestions for how to implement and use code coverage in an industrial setting.

Cited by 86

Code Coverage Status of ARC Code-DIF3D

Zhaopeng Zhong, Michael Smith, Changho Lee · 2023

The Argonne Reactor Code (ARC) software system supports users in their fast reactor design goals by providing neutronic, thermal-hydraulic, and structural analysis capabilities. DIF3D plays a pivotal role in the ARC system as the primary homogenized assembly neutronic calculation methodology for fast reactor problems. Over its 40 years history, ARC software usage with DIF3D has been applied to numerous fast and thermal spectrum reactor analysis projects with good to excellent comparison against experiments. With continued improvement of computation resources, many of the geometry modeling capabilities in DIF3D that were primarily used in low order schemes are not really needed anymore. Today, the diffusion and transport capabilities of DIF3D-VARIANT are primarily used in the reactor design process with some scattered usage of DIF3D-FD and DIF3D-Nodal. In recent work, the DIF3D software verification was completed for DIF3D-FD and DIF3D-VARIANT on the geometry options used in the Versatile Test Reactor project. While we can be confident that these capabilities of DIF3D are well used and thus trusted, it does not demonstrate that all possible input options of DIF3D are actually working, but just those that were tested as part of VTR are and that they are correct. Thus, the purpose of the present work is to identify a set of test problems for DIF3D and assess the code coverage of DIF3D for those test problems. The goal is to document what parts of the existing DIF3D code are touched by the set of test problems and which are not. Because the verification work done on DIF3D-VARIANT and DIF3D-FD was focused on the most common uses of DIF3D for fast reactor analysis, the code coverage assessment of those capabilities is the highest priority. This will ensure that nothing is being missed by the existing verification test problems that DIF3D relies upon. The DIF3D-Nodal capability will also be inspected for code coverage as part of this work to further ensure that regular regression testing of DIF3D will trap any likely errors the end user might experience with the DIF3D software. The code coverage analysis of DIF3D was performed with the Code Coverage Tool of the Intel Fortran compiler which requires modifications to the compilation of DIF3D. The detailed coverage tables are given for each submodule of DIF3D separately, and for the submodules which are primarily developed for DIF3D, most of the source files could be at least partially touched. Most of the uncovered parts/files could be easily ignored, because they are either for error message and debugging output or obviously not needed by DIF3D. Out of the entire source codes of DIF3D, only a few uncovered modules deserve further investigation.

Cited by 0Open access

Productive Coverage: Improving the Actionability of Code Coverage

Marko Ivanković, Goran Petrović, Yana Kulizhskaya et al. · 2024

Code coverage is an intuitive and widely-used test adequacy measure. Established coverage measures treat each test goal (e.g., statement or branch) as equally important, and code-coverage adequacy requires every test goal to be covered. However, this is in contrast to how code coverage is used in practice. As a result, simply visualizing uncovered code is not actionable, and developers have to manually reason which uncovered code is critical to cover with tests and which code can be left untested. To make code coverage more actionable and further improve coverage in our codebase, we developed Productive Coverage --- a novel approach to code coverage that guides developers to uncovered code that should be tested by (unit) tests. Specifically, Productive Coverage identifies uncovered code that is similar to existing tested and/or frequently in production executed code. We implemented and evaluated Productive Coverage for four programming languages (C++, Java, Go, and Python), and our evaluation shows: (1) The developer sentiment, measured at the point of use, is strongly positive; (2) Productive Coverage meaningfully increases test quality, compared to a strong baseline; (3) Productive Coverage has no negative effect on code authoring efficiency; (4) Productive Coverage modestly improves code-review efficiency; (5) Productive Coverage improves code quality and prevents defects from being introduced into the code.

Cited by 6Open access

Using Formal Coverage Analyzer for Code Coverage Improvement

Y.A. Tatarnikov · Problems of advanced micro- and nanoelectronic systems development · 2018

This presentation summarizes the results of using the tool grounded on formal proof technics (in this case the tool is Synopsys Formal Coverage Analyzer (FCA)) to improve code coverage for two design Blocks. The goal is to find unreachable coverage constructs (UNRs) in the target design Blocks and remove them from the list of uncovered constructs. The removal of UNRs saves the Designers and Verification Engineers the time needed to achieve high level of code coverage.

Cited by 0Open access

Code Coverage Status of the ARC Code DIF3D

USDOE Office of Nuclear Energy (NE), Zhaopeng Zhong, M. J. H. Smith et al. · 2025

The Argonne Reactor Code (ARC) software system supports users in their fast reactor design goals by providing neutronic, thermal-hydraulic, and structural analysis capabilities. DIF3D plays a pivotal role in the ARC system as the primary homogenized assembly neutronic calculation methodology for fast reactor problems. Over its 40 years history, ARC software usage with DIF3D has been applied to numerous fast and thermal spectrum reactor analysis projects with good to excellent comparison against experiments. With continued improvement of computation resources, many of the geometry modeling capabilities in DIF3D that were primarily used in low order schemes are not really needed anymore. Today, the diffusion and transport capabilities of DIF3D-VARIANT are primarily used in the reactor design process with some scattered usage of DIF3D-FD and DIF3D-Nodal. In recent work, the DIF3D software verification was completed for DIF3D-FD and DIF3D-VARIANT on the geometry options used in the Versatile Test Reactor project. While we can be confident that these capabilities of DIF3D are well used and thus trusted, it does not demonstrate that all possible input options of DIF3D are actually working, but just those that were tested as part of VTR are and that they are correct. Thus, the purpose of the present work is to identify a set of test problems for DIF3D and assess the code coverage of DIF3D for those test problems. The goal is to document what parts of the existing DIF3D code are touched by the set of test problems and which are not. Because the verification work done on DIF3D-VARIANT and DIF3D-FD was focused on the most common uses of DIF3D for fast reactor analysis, the code coverage assessment of those capabilities is the highest priority. This will ensure that nothing is being missed by the existing verification test problems that DIF3D relies upon. The DIF3D-Nodal capability will also be inspected for code coverage as part of this work to further ensure that regular regression testing of DIF3D will trap any likely errors the end user might experience with the DIF3D software. The code coverage analysis of DIF3D was performed with the Code Coverage Tool of the Intel Fortran compiler which requires modifications to the compilation of DIF3D. The detailed coverage tables are given for each submodule of DIF3D separately, and for the submodules which are primarily developed for DIF3D, most of the source files could be at least partially touched. Most of the uncovered parts/files could be easily ignored, because they are either for error message and debugging output or obviously not needed by DIF3D. Out of the entire source codes of DIF3D, only a few uncovered modules deserve further investigation.

Cited by 0Open access