Productivity paradoxes revisited

Duarte, Carlos Henrique C.

doi:10.1007/s10664-016-9453-5

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…We deemed the question acceptable for our purposes because the respondents' considerations when answering the questions were congruent with our expectations about the meaning of productivity, encompassing issues regarding workflow, work output, being in the zone or flow, happiness, goals achieved, coding efficiency, progress, and minimizing waste. Although we do not analyze them in this paper, the survey also included four additional, narrower measures of productivity, drawn from prior work [2], [4], [5].…”

Section: Self-rated Productivitymentioning

confidence: 99%

What Predicts Software Developers’ Productivity?

Murphy-Hill

Jaspan

Sadowski

et al. 2021

IIEEE Trans. Software Eng.

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Organizations have a variety of options to help their software developers become their most productive selves, from modifying office layouts, to investing in better tools, to cleaning up the source code. But which options will have the biggest impact? Drawing from the literature in software engineering and industrial/organizational psychology to identify factors that correlate with productivity, we designed a survey that asked 622 developers across 3 companies about these productivity factors and about self-rated productivity. Our results suggest that the factors that most strongly correlate with self-rated productivity were non-technical factors, such as job enthusiasm, peer support for new ideas, and receiving useful feedback about job performance. Compared to other knowledge workers, our results also suggest that software developers' self-rated productivity is more strongly related to task variety and ability to work remotely.

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Section: Self-rated Productivitymentioning

confidence: 99%

What Predicts Software Developers’ Productivity?

Murphy-Hill

Jaspan

Sadowski

et al. 2021

IIEEE Trans. Software Eng.

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“…Duarte [45] Productivity paradoxes revisited Sample Study Investigates the relationship between quality maturity levels and labor productivity, using a sample data set of 687 firms.…”

Section: Lokan Andmentioning

confidence: 99%

The ABC of Software Engineering Research

Stol

Fitzgerald

2018

ACM Trans. Softw. Eng. Methodol.

176

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A variety of research methods and techniques are available to SE researchers, and while several overviews exist, there is consistency neither in the research methods covered nor in the terminology used. Furthermore, research is sometimes critically reviewed for characteristics inherent to the methods. We adopt a taxonomy from the social sciences, termed here the ABC framework for SE research, which offers a holistic view of eight archetypal research strategies. ABC refers to the research goal that strives for generalizability over Actors (A) and precise measurement of their Behavior (B), in a realistic Context (C). The ABC framework uses two dimensions widely considered to be key in research design: the level of obtrusiveness of the research and the generalizability of research findings. We discuss metaphors for each strategy and their inherent limitations and potential strengths. We illustrate these research strategies in two key SE domains, global software engineering and requirements engineering, and apply the framework on a sample of 75 articles. Finally, we discuss six ways in which the framework can advance SE research.

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“…The economic interest regarding the study and measurement of productivity has allowed for comparing and proposing public policies aimed at industry sectors [2]. In the software engineering field, a software productivity measure is delivery speed, which measures the speed achieved in delivering a quantity of software (i.e., size) over a period of time [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

et al. 2020

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A fundamental issue of the software engineering economics is productivity. In this regard, one measure of software productivity is delivery speed. Software productivity prediction is useful to determine corrective activities, as well as to identify improvement alternatives. A type of software maintenance is enhancement. In this paper, we propose a data analytics-based software engineering algorithm called search method based on feature construction (SMFC) for predicting the delivery speed of software enhancement projects. The SMFC belongs to the minimalist machine learning paradigm, and as such it always generates a two-dimensional model. Unlike the usual data analytics methods, SMFC includes an original algorithmic training procedure, in which both the independent and dependent variables are considered for transformation. SMFC prediction performance is compared to those of statistical regression, neural networks, support vector regression, and fuzzy regression. To do this, seven datasets of software enhancement projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2017 were used. The validation method is leave-one-out cross validation, whereas absolute residuals have been chosen as the performance measure. The results indicate that the SMFC is statistically better than statistical regression. This fact represents an obvious advantage in favor of SMFC, because the other two methods are not statistically better than SMFC.

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Productivity paradoxes revisited

Cited by 10 publications

References 22 publications

What Predicts Software Developers’ Productivity?

What Predicts Software Developers’ Productivity?

The ABC of Software Engineering Research

A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects

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