Finest Magic Cloth or a Naked Emperor? The SKQuest Data Set on Software Metrics for Improving Transparency and Quality
Software development has a problem with transparency/visibility. As an intangible product, software and its intermediate development results are hard to see or touch. Customers of custom software have difficulties checking progress, and risk coming out with costly but low-quality software. In the sp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Standards |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2305-6703/3/2/12 |
| _version_ | 1797592590298644480 |
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| author | Christian R. Prause Ralf Gerlich |
| author_facet | Christian R. Prause Ralf Gerlich |
| author_sort | Christian R. Prause |
| collection | DOAJ |
| description | Software development has a problem with transparency/visibility. As an intangible product, software and its intermediate development results are hard to see or touch. Customers of custom software have difficulties checking progress, and risk coming out with costly but low-quality software. In the space domain with its often expensive and one-of-a-kind devices, which are developed in complex multitier supply chains, the risk is even greater. This paper presents the SKQuest data set. It contains the completed responses with 190 variables from an empirical study with over 100 software experts. The data set covers distinct aspects of measuring metrics and transparency in software projects. To show what information lies in the data set, the paper investigates, and affirms, from different perspectives, the following questions: Is transparency a problem in software development projects? Is there a desire for more transparency in projects? Can metrics contribute to improving the situation? Moreover, it attempts to replicate the results of an earlier study. The main contribution of this paper is, however, the SKQuest data set that is published with this paper in CSV formatas. It is a tool that enables systematic investigations of software metrics and allows research on how they can improve the efficiency of the software lifecycle, not limited to, but particularly with respect to transparency. Consequently, the paper may serve as a starting point for future research avenues in academia and industry and help to improve existing and future standards in software development. |
| first_indexed | 2024-03-11T01:56:19Z |
| format | Article |
| id | doaj.art-b828dfcb523c4851a35d2a7ca1dd7abe |
| institution | Directory Open Access Journal |
| issn | 2305-6703 |
| language | English |
| last_indexed | 2024-03-11T01:56:19Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Standards |
| spelling | doaj.art-b828dfcb523c4851a35d2a7ca1dd7abe2023-11-18T12:39:04ZengMDPI AGStandards2305-67032023-05-013213616810.3390/standards3020012Finest Magic Cloth or a Naked Emperor? The SKQuest Data Set on Software Metrics for Improving Transparency and QualityChristian R. Prause0Ralf Gerlich1German Aerospace Center (DLR), German Space Agency, 53227 Bonn, GermanyDr. Rainer Gerlich System and Software Engineering, 88090 Immenstaad, GermanySoftware development has a problem with transparency/visibility. As an intangible product, software and its intermediate development results are hard to see or touch. Customers of custom software have difficulties checking progress, and risk coming out with costly but low-quality software. In the space domain with its often expensive and one-of-a-kind devices, which are developed in complex multitier supply chains, the risk is even greater. This paper presents the SKQuest data set. It contains the completed responses with 190 variables from an empirical study with over 100 software experts. The data set covers distinct aspects of measuring metrics and transparency in software projects. To show what information lies in the data set, the paper investigates, and affirms, from different perspectives, the following questions: Is transparency a problem in software development projects? Is there a desire for more transparency in projects? Can metrics contribute to improving the situation? Moreover, it attempts to replicate the results of an earlier study. The main contribution of this paper is, however, the SKQuest data set that is published with this paper in CSV formatas. It is a tool that enables systematic investigations of software metrics and allows research on how they can improve the efficiency of the software lifecycle, not limited to, but particularly with respect to transparency. Consequently, the paper may serve as a starting point for future research avenues in academia and industry and help to improve existing and future standards in software development.https://www.mdpi.com/2305-6703/3/2/12software metricssoftware measurementkey performance indicatorsproject managementsoftware qualityempirical study |
| spellingShingle | Christian R. Prause Ralf Gerlich Finest Magic Cloth or a Naked Emperor? The SKQuest Data Set on Software Metrics for Improving Transparency and Quality Standards software metrics software measurement key performance indicators project management software quality empirical study |
| title | Finest Magic Cloth or a Naked Emperor? The SKQuest Data Set on Software Metrics for Improving Transparency and Quality |
| title_full | Finest Magic Cloth or a Naked Emperor? The SKQuest Data Set on Software Metrics for Improving Transparency and Quality |
| title_fullStr | Finest Magic Cloth or a Naked Emperor? The SKQuest Data Set on Software Metrics for Improving Transparency and Quality |
| title_full_unstemmed | Finest Magic Cloth or a Naked Emperor? The SKQuest Data Set on Software Metrics for Improving Transparency and Quality |
| title_short | Finest Magic Cloth or a Naked Emperor? The SKQuest Data Set on Software Metrics for Improving Transparency and Quality |
| title_sort | finest magic cloth or a naked emperor the skquest data set on software metrics for improving transparency and quality |
| topic | software metrics software measurement key performance indicators project management software quality empirical study |
| url | https://www.mdpi.com/2305-6703/3/2/12 |
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