Maintenance Effort Estimation for Open Source Software: A Systematic Literature Review

Wu, Hong; Shi, Lin; Chen, Celia; Wang, Qing; Boehm, Barry

doi:10.1109/icsme.2016.87

Cited by 28 publications

(13 citation statements)

References 32 publications

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“…A recent and growing push towards standardising methods for data sharing and publishing may lead to errors being identified more systematically as more people have access to data. One of the advantages of open-source software is that bugs are reported by users, and their correction is, at times, a community effort which allows software to be improved quickly (Wu et al, 2016). Sharing of geoscientific models' source code, although still a fairly recent development compared to the field of engineering software, has equally led to model improvements through the identification and fixing of bugs beyond the model development teams .…”

Section: Discussionmentioning

confidence: 99%

Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data

Ménard¹,

Essery

Barr

et al. 2019

Earth Syst. Sci. Data

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Abstract. This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites. The long-term datasets (one maritime, one arctic, three boreal, and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature, and surface temperature) available at varying temporal intervals. Thirty-year (1980–2010) time series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to 1 h time steps and bias-corrected. Although the correction was applied to all sites, it was most important for mountain sites hundreds of metres higher than the grid elevations and for which uncorrected air temperatures were too high and snowfall amounts too low. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency, and reproducibility in geosciences. The Supplement provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors. The data are published in the repository PANGAEA and are available at https://doi.pangaea.de/10.1594/PANGAEA.897575.

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Section: Discussionmentioning

confidence: 99%

Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data

Ménard¹,

Essery

Barr

et al. 2019

Earth Syst. Sci. Data

View full text Add to dashboard Cite

show abstract

“…A recent and growing push towards standardising methods for data sharing and publishing may lead to errors being identified more systematically as more people have access to data. One of the advantages of open source software is that bugs are reported by users and their correction is, at times, a community effort which allows software to be improved quickly (Wu et al, 2016). Sharing of 440 geoscientific models' source code, although still a fairly recent development compared to the field of engineering software, has equally led to model improvements through the identification and fixing of bugs beyond the model development teams (David et al, 2016;Samuel Morin, personal communication about the Crocus snow model).…”

Section: Discussionmentioning

confidence: 99%

Meteorological and evaluation datasets for snow modelling at ten reference sites: description of in situ and bias-corrected reanalysis data

Ménard¹,

Essery²,

Barr³

et al. 2019

Preprint

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Abstract. This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions modelling at ten sites. The long-term datasets (one maritime, one arctic, three boreal and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between seven and twenty years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature and surface temperature) available at varying temporal intervals. 30-year (1980–2010) time-series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to one-hour timesteps and bias corrected. Although applied to all sites, the bias corrections are particularly important for mountain sites that are hundreds of meters higher than the grid elevations; as a result, uncorrected air temperatures are too high and snowfall amounts are too low in comparison with in situ measurements. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency and reproducibility in Geosciences. Supplementary material provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors. The data are published in the repository PANGAEA and available at: https://doi.org/10.1594/PANGAEA.897575.

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“…• Automated analysis: Automatic analysis involves analyzing source code or other software artifacts and quantifying software maintainability into numeric results. This includes static code analysis such as measuring Maintainability Index, technical debt, code smells, and other Object-Oriented metrics [4,5], as well as bug-focused metrics such as bug fixing time [6] and accumulated defect density [7].…”

Section: Introductionmentioning

confidence: 99%

“…Although bug fixing times may reflect maintenance effort [6], these bug-focused metrics also do not provide a systematic understanding of software maintainability. Furthermore, these metrics do not utilize the information provided by the natural language descriptions due to their unstructured nature.…”

Section: Introductionmentioning

confidence: 99%

A Large Empirical Study on Automatically Classifying Software Maintainability Concerns from Issue Summaries

Chen¹,

Shoga²

2021

Adv. sci. technol. eng. syst. j.

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Software maintenance contributes the majority of software system life cycle costs. However, existing approaches with automated code analysis are limited by accuracy and scope. Using human-assessed methods or implementing quality standards are more comprehensive alternatives, but they are much more costly for smaller organizations, especially in opensource software projects. Instead, bugs are generally used to assess software quality, such as using bug fixing time as an estimate of maintenance effort. Although associated bug reports contain useful information that describe software faults, the content of these bug reports are rarely used. In this paper, we incorporate quality standards with natural language processing techniques to provide insight into software maintainability using the content of bug reports and feature requests. These issues are classified with an automated approach into various maintainability concerns whose generalizability has been validated against over 6000 issue summaries extracted from nine open source projects in previous works. Using this approach, we perform a large empirical study of 229,329 issue summaries from 61 different projects. We evaluate the differences in expressed maintainability concerns between domains, ecosystems, and types of issues. We have found differences in relative proportions across ecosystem, domain and issue severity. Further, we evaluate the evolution of maintainability across several versions in a case study of Apache Tomcat, identifying some trends within different versions and over time. In summary, our contributions include a refinement of definitions from the original empirical study on maintainability related issues, an automated approach and associated rules for identifying maintainability related quality concerns, identification of trends in the characteristics of maintainability related issue summaries through a large-scale empirical study across two major open source ecosystems, and a case study on changes in maintainability over versions in Apache Tomcat.

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Maintenance Effort Estimation for Open Source Software: A Systematic Literature Review

Cited by 28 publications

References 32 publications

Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data

Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data

Meteorological and evaluation datasets for snow modelling at ten reference sites: description of in situ and bias-corrected reanalysis data

A Large Empirical Study on Automatically Classifying Software Maintainability Concerns from Issue Summaries

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