Towards an evidence-based theoretical framework on factors influencing the software development productivity

Chapetta, Wladmir Araujo; Travassos, Guilherme Horta

doi:10.1007/s10664-020-09844-5

Cited by 11 publications

(7 citation statements)

References 104 publications

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“…The lack of new research is a risk of any scientific discipline. It jeopardizes the building of news ideas and strengthens the prevailing ones, which prevents accelerating the pace of knowledge acquisition in a theme [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

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Quantitative Metrics for Performance Monitoring of Software Code Analysis Accredited Testing Laboratories

Chapetta

Neves

Machado

2021

Sensors

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Modern sensors deployed in most Industry 4.0 applications are intelligent, meaning that they present sophisticated behavior, usually due to embedded software, and network connectivity capabilities. For that reason, the task of calibrating an intelligent sensor currently involves more than measuring physical quantities. As the behavior of modern sensors depends on embedded software, comprehensive assessments of such sensors necessarily demands the analysis of their embedded software. On the other hand, interlaboratory comparisons are comparative analyses of a body of labs involved in such assessments. While interlaboratory comparison is a well-established practice in fields related to physical, chemical and biological sciences, it is a recent challenge for software assessment. Establishing quantitative metrics to compare the performance of software analysis and testing accredited labs is no trivial task. Software is intangible and its requirements accommodate some ambiguity, inconsistency or information loss. Besides, software testing and analysis are highly human-dependent activities. In the present work, we investigate whether performing interlaboratory comparisons for software assessment by using quantitative performance measurement is feasible. The proposal was to evaluate the competence in software code analysis activities of each lab by using two quantitative metrics (code coverage and mutation score). Our results demonstrate the feasibility of establishing quantitative comparisons among software analysis and testing accredited laboratories. One of these rounds was registered as formal proficiency testing in the database—the first registered proficiency testing focused on code analysis.

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Section: Discussionmentioning

confidence: 99%

“…Scopus database is the initial and unique information source to perform the search, because there is an intersection and, consequently, a lot of duplicate returned articles, when compared to other databases, such as IEEE Xplore, Engineering Village, ACM Digital Library, etc. [ 51 ].…”

Section: Methodsmentioning

confidence: 99%

Quantitative Metrics for Performance Monitoring of Software Code Analysis Accredited Testing Laboratories

Chapetta

Neves

Machado

2021

Sensors

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“…It is hard to find examples of SE researchers adopting these more sophisticated approaches to integrate qualitative models with quantitative review findings. Albeit, we are aware of a synthesis method called Structured Synthesis Method (SSM) [70], [71], [72] that is intended to allow the combination of different types of evidence.…”

Section: Prisma For Mixed-methods Studiesmentioning

confidence: 99%

SEGRESS: Software Engineering Guidelines for REporting Secondary Studies

Kitchenham

Madeyski

Budgen

2023

IIEEE Trans. Software Eng.

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Context: Several tertiary studies have criticized the reporting of software engineering secondary studies. Objective: Our objective is to identify guidelines for reporting software engineering (SE) secondary studies which would address problems observed in the reporting of software engineering systematic reviews (SRs). Method: We review the criticisms of SE secondary studies and identify the major areas of concern. We assess the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement as a possible solution to the need for SR reporting guidelines, based on its status as the reporting guideline recommended by the Cochrane Collaboration whose SR guidelines were a major input to the guidelines developed for SE. We report its advantages and limitations in the context of SE secondary studies. We also assess reporting guidelines for mapping studies and qualitative reviews, and compare their structure and content with that of PRISMA 2020. Results: Previous tertiary studies confirm that reports of secondary studies are of variable quality. However, ad hoc recommendations that amend reporting standards may result in unnecessary duplication of text. We confirm that the PRISMA 2020 statement addresses SE reporting problems, but is mainly oriented to quantitative reviews, mixed-methods reviews and meta-analyses. However, we show that the PRISMA 2020 item definitions can be extended to cover the information needed to report mapping studies and qualitative reviews. Conclusions: In this paper and its Supplementary Material, we present and illustrate an integrated set of guidelines called SEGRESS (Software Engineering Guidelines for REporting Secondary Studies), suitable for quantitative systematic reviews (building upon PRISMA 2020), mapping studies (PRISMA-ScR), and qualitative reviews (ENTREQ and RAMESES), that addresses reporting problems found in current SE SRs.

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“…Organization Software risk exposure level represents the level of project uncertainty, having a noticeable impact on how the software can respond to business needs over time [62]. 0.075 Jiang et al [65] considered, in open-source software development projects, the correlation between the number of commits and the number of contributors as the main factor for productivity.…”

Section: 46mentioning

confidence: 99%

“…the construction of a work environment that favors the commitment and interest of team members are factors of positive impact on productivity[22].1.15Organization Merits and rewards system these systems contribute positively to the productivity of the development team[22]. 1.098Organization Accuracy of information the accuracy of the information that reaches the development team (such as bug reports, use cases, and change requests) influences its productivity[27] 1.098Organization Team autonomy level it subjectively represents the extent to which the software team has authority and control in making decisions to carry out the project[62].…”

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confidence: 99%

Tool for Measuring Productivity in Software Development Teams

2021

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Despite efforts to define productivity, there is no consensus in the software industry regarding what the term productivity means and, instead of having only one metric or factor that describes productivity, it is defined by a set of aspects. Our objective is to develop a tool that supports the productivity measurement of software development teams according to the factors found in the literature. We divided these factors into four groups: People, Product, Organization, and Open Source Software Projects. We developed a web system containing the factors that influence productivity identified in this work, called Productive, to support software development teams in measuring their productivity. After developed the tool, we monitored its use over eight weeks with two small software development teams. From the results, we found that software development companies can use the system to support monitoring team productivity. The results also point to an improvement in productivity while using the system, and a survey applied to users demonstrates the users’ positive perception regarding the results obtained. In future work, we will monitor the use of the tool and investigate the users’ perceptions in other project contexts.

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Towards an evidence-based theoretical framework on factors influencing the software development productivity

Cited by 11 publications

References 104 publications

Quantitative Metrics for Performance Monitoring of Software Code Analysis Accredited Testing Laboratories

Quantitative Metrics for Performance Monitoring of Software Code Analysis Accredited Testing Laboratories

SEGRESS: Software Engineering Guidelines for REporting Secondary Studies

Tool for Measuring Productivity in Software Development Teams

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