A replicated study on correlating agile team velocity measured in function and story points

Huijgens, Hennie; Solingen, Rini van

doi:10.1145/2593868.2593874

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…sprint velocity (SV) is the number of SP completed by the product development team in one sprint [34]. Several researchers have studied and analyzed the use of SV in software projects [50][51][52]. Although agile teams use other metrics, the most influential metrics in many of the studies are SV and effort estimate [53].…”

Section: Productivitymentioning

confidence: 99%

Comparing Measured Agile Software Development Metrics Using an Agile Model-Based Software Engineering Approach versus Scrum Only

2023

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This study compares the reliability of estimation, productivity, and defect rate metrics for sprints driven by a specific instance of the agile approach (i.e., scrum) and an agile model-Bbased software engineering (MBSE) approach called the integrated Scrum Model-Based System Architecture Process (sMBSAP) when developing a software system. The quasi-experimental study conducted ten sprints using each approach. The approaches were then evaluated based on their effectiveness in helping the product development team estimate the backlog items that they could build during a time-boxed sprint and deliver more product backlog items (PBI) with fewer defects. The commitment reliability (CR) was calculated to compare the reliability of estimation with a measured average scrum-driven value of 0.81 versus a statistically different average sMBSAP-driven value of 0.94. Similarly, the average sprint velocity (SV) for the scrum-driven sprints was 26.8 versus 31.8 for the MBSAP-driven sprints. The average defect density (DD) for the scrum-driven sprints was 0.91, while that of the sMBSAP-driven sprints was 0.63. The average defect leakage (DL) for the scrum-driven sprints was 0.20, while that of the sMBSAP-driven sprints was 0.15. The t-test analysis concluded that the sMBSAP-driven sprints were associated with a statistically significant larger mean CR, SV, DD, and DL than that of the scrum-driven sprints. The overall results demonstrate formal quantitative benefits of an agile MBSE approach compared to an agile alone, thereby strengthening the case for considering agile MBSE methods within the software development community. Future work might include comparing agile and agile MBSE methods using alternative research designs and further software development objectives, techniques, and metrics.

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Section: Productivitymentioning

confidence: 99%

Comparing Measured Agile Software Development Metrics Using an Agile Model-Based Software Engineering Approach versus Scrum Only

2023

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“…As a result, they found a positive correlation between functional size and number of story points for 2191 stories and 18 iterations. Huijgens & Solingen presented a replicated study of Santana et al's work [23] in a project for a Dutch banking organization and analyzed the relationship between story points and function points [24]. They measured the functional size of 14 releases with NESMA [25] guidelines and estimated story points.…”

Section: Literaturementioning

confidence: 99%

Effort estimation for agile software development

Salmanoğlu

Hacaloğlu

Demirörs

2017

Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process An

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Agile methodologies have gained significant popularity among software development organizations during the last decade. Although agile methodologies are regarded as minimizing formal processes, they still utilize an estimation methodology for proper management. Story point is the most common input for agile effort estimation. Story point is an arbitrary measure; it reflects experiences of project participants. On the other hand, functional size is an alternative measure used in practice as an input for effort estimation. In this research, we collect and present the outcomes of three case studies which compared the effectiveness of COSMIC-based and story point based effort estimation in agile context. On selected projects of these organizations, software functional size was measured with COSMIC functional size measurement methodology. Effort prediction models were formed by using COSMIC size and actual effort spent; and the models were tested in terms of their effectiveness. The results show controversial outcomes. For all the cases, COSMIC based estimation was more precise. Therefore, COSMIC is an appropriate measure to estimate the effort in organizations that adopt agile software development. It is also observed that COSMIC allowed for computing productivity which has less disperse distribution than the productivity computed with SP. The data is also provided to help other researchers conduct their own studies.

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“…A replicated study on correlating agile team velocity measured in function and story points. Proceedings of the 5th International Workshop on Emerging Trends in Software Metrics (WETSoM 2014) (Huijgens & van Solingen, 2014a). 3.…”

Section: Publications Not Included In the Thesismentioning

confidence: 99%

Evidence-based software portfolio management

Huijgens

2016

Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering

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While many studies can be found on estimating the cost (or effort) of software deliveries (Jørgensen & Shepperd, 2007), only a handful exists about the price thereof. For pricing purposes in a commercial context where software delivery is partly performed by suppliers, most companies rely heavily on expert judgment (Boehm, 1984) (Jørgensen, 2004). This is not always a successful approach (Moløkken & Jørgensen, 2003), and software development is often characterized by high cost and schedule overruns (Verhoef, 2002) (Glass, 2002), despite a huge number of tools developed over time to support project estimation (Boehm, 1984

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A replicated study on correlating agile team velocity measured in function and story points

Cited by 11 publications

References 14 publications

Comparing Measured Agile Software Development Metrics Using an Agile Model-Based Software Engineering Approach versus Scrum Only

Comparing Measured Agile Software Development Metrics Using an Agile Model-Based Software Engineering Approach versus Scrum Only

Effort estimation for agile software development

Evidence-based software portfolio management

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