Optimizing the Self-Organizing Team Size Using a Genetic Algorithm in Agile Practices

Almadhoun, Wael; Hamdan, Mohammad

doi:10.1515/jisys-2018-0085

Cited by 7 publications

(9 citation statements)

References 44 publications

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“…Ensemble learning into the optimization algorithm reveals that the optimal team size and distribution for a given total number of team members range between six and eight members per team, which aligns with industry practices [15][16][17]. These results are practically equal to those reported in [11].…”

Section: Introductionmentioning

confidence: 51%

“…Although this question has been approached with anecdotal approximations like the two-pizza rule (a team should not be larger than the number of people two pizzas can feed) [10], there are various factors in the interactions among team members that can influence the team's size. A systematic approach to this problem is proposed in [11]. In this study, the challenge is addressed as an optimization problem: given the number of professionals, how to organize them to optimize the number of communication channels, considering both the channels within the team and between teams.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices

Olivares,

Noel,

Guzmán

et al. 2024

Biomimetics

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One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms.

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Section: Introductionmentioning

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices

Olivares,

Noel,

Guzmán

et al. 2024

Biomimetics

View full text Add to dashboard Cite

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“…Team size : A growing body of literature recognizes the importance of team size in agile project teams (e.g., Lalsing et al, 2012). While some scholars have proposed ideal team sizes of no more than nine people (Almadhoun & Hamdan, 2020), studies have increasingly emphasized boundary conditions and necessary modifications that enable organizations to scale agile methods for larger clusters of employees (see Alqudah & Razali, 2016 for a detailed review in the domain of software development). Since communication and collaborative decision‐making are easier in smaller rather than larger teams, a recent systematic review suggests that agile methods are particularly suited to small to medium teams (Keshta & Morgan, 2017).…”

Section: Theorymentioning

confidence: 99%

The affective, behavioural and cognitive outcomes of agile project management: A preliminarymeta‐analysis

Koch

Drazic

Schermuly

2023

J Occupat & Organ Psyc

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Agile project management (APM) refers to a set of project management frameworks that are increasingly common in a range of industries. Even though the positive effects of APM for employees and organizations are widely assumed, no systematic account of affective, behavioural and cognitive outcomes appears in the academic literature. In this pre‐registered meta‐analysis, we examine the effects of APM based on K = 40 records (with k = 41 independent studies, N = 73,825). Our analyses indicate the beneficial effects of APM across outcomes: For the affective outcomes of job satisfaction, affective strain and organizational commitment, the effect sizes were, on average, small. For the behavioural outcomes of performance and innovative behaviour, the effect sizes were medium to large. For the cognitive outcome of psychological empowerment, the results of our meta‐analysis suggest a medium effect. Furthermore, we explore the moderating effects of contextual (team size, occupational groups, culture) and methodological boundary conditions (publication status, study design, levels of analysis, study quality). Compared to studies conducted with software developers, the effect sizes are stronger in other occupations, such as manufacturing, health care and logistics. Moreover, stronger effect sizes are found in published than in unpublished records. We conclude by discussing the state of quantitative APM research and highlight avenues for future study.

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“…It has recently been feasible to speed up the genetic processes and break out of local optimal states by adapting operator probabilities in GAs. However, building adaptive GAs is a difficult task, and the vast majority of methods used are heuristic in their approach [11] IV. CLUSTERING ALGORITHMS Methods like partitioning and hierarchical structure are utilized in clustering approaches [12].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Genetic Algorithm Based Clustering Optimization A Survey

Nadhum

2023

Jour. Kufa Math. Comp.

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Genetic algorithm has an important role in improving clustering methods. When it comes to getting into a dataset, one of the most common methods used is clustering. Recent years have seen a rise in the number of articles interested in clustering, which may be attributed to the development of various new fields of application. Most clustering method's performance depends on initial values of parameters or the preprocessing of a dataset, so clustering needs to optimize. The improvement of clustering in two directions, either we improve the parameters or improve the input data. Ga is an evolutionary technique well-known in optimization. This survey deals with the methods that use a genetic algorithm to choose parameter values and input data. This study shows many important performance metrics used to get the optimal result in each research.

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Optimizing the Self-Organizing Team Size Using a Genetic Algorithm in Agile Practices

Cited by 7 publications

References 44 publications

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices

The affective, behavioural and cognitive outcomes of agile project management: A preliminarymeta‐analysis

Genetic Algorithm Based Clustering Optimization A Survey

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