A Bayesian networks-based approach to assess and improve the teamwork quality of agile teams

Freire, Arthur Silva; Perkusich, Mirko; Saraiva, Renata M.; Almeida, Hyggo; Perkusich, Angelo

doi:10.1016/j.infsof.2018.04.004

Cited by 31 publications

(40 citation statements)

References 28 publications

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“…TWQ is an important factor or mediating variable for studying the rationality of team structure and team integration in forming cooperation mechanisms and their impact on team performance, team success and sustainability. It has been gaining more and more traction in the academic community [18][19][20][21][22]. Goebel, Guo and Wood explored how medical experts work together in interdisciplinary teams to achieve a good quality of collaboration [18].…”

Section: Teamwork Quality (Twq)mentioning

confidence: 99%

“…The theoretical model of teamwork quality (TWQ) is often used to study the relationship between teamwork factors and team performance, team members' personal success, team success and sustainability, including the research and evaluation of a research and development (R&D) team in enterprises [8,[12][13][14][15][16], medical care teams [18,19], learning groups in education [20], and agile team research in software development [21,22]. However, although previous studies have used the TWQ model to explain the relationship between teamwork and different teams, few studies have examined high-level research and innovation teams, especially those working in the frontier areas of basic research and in innovative groups funded by government funds.…”

Section: Introductionmentioning

confidence: 99%

“…However, although previous studies have used the TWQ model to explain the relationship between teamwork and different teams, few studies have examined high-level research and innovation teams, especially those working in the frontier areas of basic research and in innovative groups funded by government funds. At the same time, in the relevant literature, many studies have examined scientific research organizations and teamwork [22][23][24][25][26], but few have used the TWQ model to analyze the impact of team structure and teamwork on a team's success and sustainability.…”

Section: Introductionmentioning

confidence: 99%

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Examining the Factors Behind the Success and Sustainability of China’s Creative Research Group: An Extension of the Teamwork Quality Model

Gao

Xie

2019

Sustainability

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The Creative Research Group (CRG) is the special high-level scientific and innovation team funded by the National Natural Science Foundation of China to promote basic research at the frontiers of science. In general, there are problems of “structure anomie” and “cooperation inefficiency” in the operations and teamwork dynamics within the nationwide CRG project. By extending and developing the two-stage teamwork quality model, this study aims to depict and analyze the impact factors behind the success and sustainability of the Creative Research Group (SSCRG), and reveal the relationships among them. Herein, rationality of team structure (ROTS) is used to describe the rationality of team formation and structure collocation, teamwork quality (TWQ) is used to describe the process of team members’ integration and cooperation, and SSCRG includes team performance, personal success and team comprehensive impacts. The results show that ROTS and TWQ have different influences on SSCRG, while TWQ is the key mediation factor between ROTS and SSCRG. In summary, the model built describes the complex phenomena and relationships in the teamwork of the CRG, and helps us to understand and solve the problems of structure and cooperation. Although the limitations lie in the specific samples and research methods, the extension and migration of classical models and theories would help to further deepen such research and contribute to the governance and development of such innovation teams.

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Section: Teamwork Quality (Twq)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Examining the Factors Behind the Success and Sustainability of China’s Creative Research Group: An Extension of the Teamwork Quality Model

Gao

Xie

2019

Sustainability

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“…In the experiment, 10 developers quantified a BN (adapted from the model proposed in Freire et al [8]) using the proposed approach and two others methods: the Weighted Sum Algorithm (WSA) and a variation of the WSA, here named WSAAHP, that employs indirect probability elicitation by means of pairwise comparisons between states in which the domain experts compare the likelihood of states using a verbal and numerical scale.…”

Section: Empirical Validationmentioning

confidence: 99%

Improving the Applicability of the Ranked Nodes Method to build Expert-Driven Bayesian Networks (S)

Nunes

Silva

Perkusich

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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One challenge in constructing a Bayesian network (BN) is defining the node probability tables (NPTs), which can be learned from data or elicited from domain experts. In practice, for large-scale BN it is common not to have enough data for learning and elicitation from experts is unfeasible. Previous work proposed a solution to this problem: the Ranked Nodes Method (RNM). However, this solution needs to be applied by a RNM expert who, through the elicitation of expert judgement, identifies the necessary parameters for the RNM algorithm to generate the NPTs. Hence, this paper presents a novel approach to define NPT using the RNM with no ranked nodes-specific knowledge. The solution is named Simulated Bayesian Network Expert (SBNE). It consists of eliciting a subset of the NPT from the domain experts which is used as input to an algorithm that estimates the optimal parameters for the RNM to generate the NPTs. To validate our solution, we conducted an experiment with multiple domain experts and compared the results with other methods. Our solution outperformed the other methods (producing NPTs at least 12% more accurate) and is, therefore, a promising approach to apply RNM without relying on RNM experts.

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“…As far as we know, no previous study has proposed a general methodology for defining SIs prediction models within the context of RSD [2]. However, there exist several studies proposing individual SIs using BNs in the area of software engineering, specifically, to estimate "teamwork quality" [3], to model quality in software projects [4], to estimate effort in software development [5], for requirements engineering [6], to predict software defects [7], and to estimate value and help decision making [8,9]. The difference between these works and ours is that we build a SI BN prediction model based upon companies' business knowledge and data automatically measured from heterogeneous data sources, and within the context of RSD.…”

Section: Introductionmentioning

confidence: 99%

Using Bayesian Networks to estimate Strategic Indicators in the context of Rapid Software Development

Manzano

Mendes

Gómez

et al. 2018

Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering

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Background: During Rapid Software Development, a large amount of project and development data can be collected from different and heterogeneous data sources. Aims: Design a methodology to process these data and turn it into relevant strategic indicators to help companies make meaningful decisions. Method: We adapt an existing methodology to create and estimate strategic indicators using Bayesian Networks in the context of Rapid Software Development, and applied it to a use case. Results: Applying the methodology in the use case, we create a model to predict product quality based on software factors and metrics, using companies' business knowledge and collected data. Conclusions: We proved the methodology's feasibility and obtained positive feedback from the company's use case. CCS CONCEPTS• Software and its engineering → Software creation and management KEYWORDS Strategic indicator, Bayesian Network, Decision Making, Rapid software development

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A Bayesian networks-based approach to assess and improve the teamwork quality of agile teams

Cited by 31 publications

References 28 publications

Examining the Factors Behind the Success and Sustainability of China’s Creative Research Group: An Extension of the Teamwork Quality Model

Examining the Factors Behind the Success and Sustainability of China’s Creative Research Group: An Extension of the Teamwork Quality Model

Improving the Applicability of the Ranked Nodes Method to build Expert-Driven Bayesian Networks (S)

Using Bayesian Networks to estimate Strategic Indicators in the context of Rapid Software Development

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