Using Data Envelopment Analysis in software development productivity measurement

Asmild, Mette; Paradi, Joseph C.; Kulkarni, Atin

doi:10.1002/spip.298

Cited by 28 publications

(20 citation statements)

References 24 publications

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“…Second, most of the previous researches have used 'effort' as the only input or one of the inputs [3,5,6,15,27,[29][30][31][32][33]. However, we use other effort such as conformance effort (EoC) and non-conformance effort (EoNC), in addition to the development effort.…”

Section: Literature Review and Contributionsmentioning

confidence: 96%

“…A majority of previous studies have used 'Effort' as the only input [3,5,6,15,27,[29][30][31][32][33] and SLOC and/or FP as output(s) [3,5,7,15,27,29]. In Sect.…”

Section: Sensitivity Analysis Of Model Specificationmentioning

confidence: 98%

“…The DEA is applied to 144 software projects to identify best performers and use them as benchmarks [33]. The authors disaggregate the output, function points, into five function types i.e.…”

Section: Literature Review and Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

Benchmarking software development productivity of CMMI level 5 projects

2015

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In this paper, data envelopment analysis variable returns to scale (DEA VRS) model is applied to data collected on 79 software development projects from a leading CMMI level 5 organization. We divide overall software effort into software development effort, software quality conformance effort (EoC), and software maintenance non-conformance (EoNC) effort due to poor software quality at delivery time. Partitioning effort into software development and software quality metrics provides us a comprehensive model to measure productivity of software projects and to identify best practice projects. Some of positive productivity drivers from the DEA best practice efficient projects point to good customer rapport and application familiarity. Inefficient projects had problems such as customer requirements volatility, and the use of unfamiliar technology. The DEA results identify 12 ''best practice'' projects that can be emulated for software process improvement. Additionally, our results point to approximately 50 % potential for productivity improvement in software projects to get to the level of ''best practice'' projects. This study shows that including EoC and EoNC as inputs has a positive impact on the best practice frontier.

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Section: Literature Review and Contributionsmentioning

confidence: 96%

“…A majority of previous studies have used 'Effort' as the only input [3,5,6,15,27,[29][30][31][32][33] and SLOC and/or FP as output(s) [3,5,7,15,27,29]. In Sect.…”

Section: Sensitivity Analysis Of Model Specificationmentioning

confidence: 98%

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Benchmarking software development productivity of CMMI level 5 projects

2015

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“…Significant research work has been devoted to the application of DEA to measure efficiency within an economic or business-related context and even to the software engineering industry (Asmild et al 2006;Stensrud and Myrtveit 2003). von Mayrhauser et al (2000) applied DEA to assess the efficiency of 46 software projects from the NASA-SEL database.…”

Section: Related Workmentioning

confidence: 99%

Benchmarking library and application software with Data Envelopment Analysis

Chatzigeorgiou

Stiakakis

2010

Software Qual J

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Library software is generally believed to be well-structured and follows certain design guidelines due to the need of continuous evolution and stability of the respective APIs. We perform an empirical study to investigate whether the design of open-source library software is actually superior to that of application software. By analyzing certain design principles and heuristics that are considered important for API design, we extract a set of software metrics that are expected to reflect the improved nature of libraries. An initial comparison by conventional statistical analysis confirms the overall belief that products of different software size scale should not be compared by simply examining metric values in isolation. In this paper, we propose the use of Data Envelopment Analysis (DEA), borrowed from production economics, as a means of measuring and benchmarking the quality of different object-oriented software designs captured by software metrics and apply this approach to the comparison of library and application software. The advantages offered by DEA and the differences between the application of DEA in an economic and a technological context are discussed. Results of the approach are presented for 44 opensource projects, equally divided between libraries and applications.

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“…Although the ideal output measure is delivered business value, either size measures such as lines of code (low-level) and function points (high-level) or requirements-oriented measures like use-cases and stories are adopted as proxies. However, each proxy has advantages and disadvantages [9]. Finding portable, meaningful, sound measures of size, functionality, productivity and quality has been an elusive endeavor.…”

Section: Advantages Limitations and Usesmentioning

confidence: 99%