Benchmarking software development productivity

Maxwell, Katrina; Forselius, Pekka

doi:10.1109/52.820015

Cited by 125 publications

(78 citation statements)

References 6 publications

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“…As was discussed in section 2, a similar productivity analysis was carried out by Maxwell and Forselius [16] ined data from a total of 206 projects from 26 companies. We refer to this as the FinnishMF data set.…”

Section: Productivity Trends Over Timementioning

confidence: 99%

“…This is our vantage point. Our main motivation is a previous study conducted by Maxwell and Forselius [16] on an older version of the same data set that we use. In their study, Maxwell and Forselius found Company and Business Sector as the most important variables in explaining productivity.…”

Section: Related Workmentioning

confidence: 99%

“…In order to make a somewhat simplistic comparison, in Table 3 we compare the reported 3 mean productivity by [16] with the 1997-onwards projects from Finnish602 (i.e. Finnish97+).…”

Section: Productivity Trends Over Timementioning

confidence: 99%

“…The next section very briefly reviews some related work including a similar, earlier study by Maxwell and Forselius [16]. Next we describe the data set used for our analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In total, 102 variables are collected, though some are difficult to analyse due to a significant proportion of missing values. A fuller description of the data set may be found in Maxwell and Forselius [16].…”

Section: Background On the "Finnish" Data Setmentioning

confidence: 99%

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An Empirical Analysis of Software Productivity over Time

Premraj

Shepperd

Kitchenham

et al.

11th IEEE International Software Metrics Symposium (METRICS'05)

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OBJECTIVE -the aim is to investigate how software project productivity has changed over time. Within this overall goal we also compare productivity between different business sectors and seek to identify major drivers. METHOD -we analysed a data set of more than 600 projects that have been collected from a number of Finnish companies since 1978. RESULTS -overall, we observed a quite pronounced improvement in productivity over the entire time period, though, this improvement is less marked since the 1990s. However, the trend is not smooth. We also observed productivity variability between company and business sector. CONCLUSIONS -whilst this data set is not a random sample so generalisation is somewhat problematic, we hope that it contributes to an overall body of knowledge about software productivity and thereby facilitates the construction of a bigger picture.

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Section: Productivity Trends Over Timementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In order to make a somewhat simplistic comparison, in Table 3 we compare the reported 3 mean productivity by [16] with the 1997-onwards projects from Finnish602 (i.e. Finnish97+).…”

Section: Productivity Trends Over Timementioning

confidence: 99%

“…The next section very briefly reviews some related work including a similar, earlier study by Maxwell and Forselius [16]. Next we describe the data set used for our analysis.…”

Section: Introductionmentioning

confidence: 99%

Section: Background On the "Finnish" Data Setmentioning

confidence: 99%

See 3 more Smart Citations

An Empirical Analysis of Software Productivity over Time

Premraj

Shepperd

Kitchenham

et al.

11th IEEE International Software Metrics Symposium (METRICS'05)

Self Cite

View full text Add to dashboard Cite

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Resource Estimation in Software Engineering

Briand

Wieczorek

2002

Encyclopedia of Software Engineering

117

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This article presents a comprehensive overview of the state of the art in software resource estimation. We describe common estimation methods and also provide an evaluation framework to systematically compare and assess alternative estimation methods. Although we have tried to be as precise and objective as possible, it is inevitable that such a comparison exercise be somewhat subjective. We however, provide as much information as possible, so that the reader can form his or her own opinion on the methods to employ. We also discuss the applications of such estimation methods and provide practical guidelines. Understanding this article does not require any specific expertise in resource estimation or quantitative modeling. However, certain method descriptions are brief and the level of understanding that can be expected from such a text depends, to a certain extent, on the reader's knowledge. Our objective is to provide the reader with a comprehension of existing software resource estimation methods as well as with the tools to reason about estimation methods and how they relate to the reader's problems. The second section (on resource estimation) briefly describes the problems at hand, the history, and the current status of resource estimation in software engineering research and practice. The third section (on overview of estimation models) provides a comprehensive, although certainly not complete, overview of resource estimation methods. Project sizing, an important issue related to resource estimation, is then discussed in the fourth section (on sizing projects). The fifth section (on framework for com parison and evaluation) defines an evaluation framework that allows us to make systematic and justified comparisons in the sixth section (on evaluation and comparison effort estimation methods). The seventh section (on considerations influencing choice of estimating method) provides guidelines regarding the selection of appropriate estimation methods, in a given context. The eighth section (on typical applications) describes typical scenarios for using resource estimation methods, thereby relating them to software management practice. The ninth section (on future directions) attempts to define important research and practice directions, requiring the collaboration of academia and industry. This article then concludes by summarizing the main points made throughout the article.

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Co-located and distributed natural-language requirements specification: traditional versus reuse-based techniques

Gea

Nicolás

Alemán

et al. 2016

J. Softw. Evol. and Proc.

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Requirements Engineering (RE) includes processes intended to elicit, analyse, specify and validate systems and software requirements throughout the software life cycle. Mastering the principles of RE is key to achieving the goals of better, cheaper and quicker systems and software development projects. It is also important to be prepared to work with remote teammates, as distributed and global projects are becoming more common. This paper presents an experiment with a total of 31 students from two universities in Spain and Morocco who were assigned to either a co-located or a distributed team. Both traditional and reuse-based requirements specification techniques were applied by the participants to produce requirements documents. Their outcomes were then analysed, and the approaches were compared from the point of view of their effect on a set of performance-based and perception-based variables in co-located and distributed settings. We found significant differences in only productivity (Z = À2.320, p = 0.020) and difficulty (Z = À2.124, p = 0.034) as regards the scores attained for non-reuse and reuse conditions, both in the co-located modality. Our findings show that, in general, the participants attained similar results for requirements specification when using the two strategies in both distributed and non-distributed environments.

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Benchmarking software development productivity

Abstract: 0 I E E E S O F T WA R E J a n u a r y / F e b r u a r y 2 0 0 0 0 7 4 0 -7 4 5 9 / 0 0 / $ 1 0 . 0 0 © 2 0 0 0 I E E E factors affect software-development productivity in any given environment.

Cited by 125 publications

References 6 publications

An Empirical Analysis of Software Productivity over Time

An Empirical Analysis of Software Productivity over Time

Resource Estimation in Software Engineering

Co-located and distributed natural-language requirements specification: traditional versus reuse-based techniques

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