Software Development Process Simulation: Multi Agent-Based Simulation versus System Dynamics

Cherif, Redha; Davidsson, Paul

doi:10.1007/978-3-642-13553-8_7

Cited by 12 publications

(2 citation statements)

References 13 publications

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“…For similar reasons, ABS models are better at modeling different developers in details. For example, Cherif and Davidsson [46] built an ABS model that simulates the difficulty of task j as the difference between level of knowledge b ij and the required level of knowledge θ j , where b ij denotes developer i 's knowledge about activity j. Cherif and Davidsson compared the ABS model with SD model and indicated that the main difference is that an SD model inputs average value of individual characteristics (manpower property) as an alternative.…”

Section: Comparison Of Different Modeling Paradigms In Modelingmentioning

confidence: 99%

Metrics for Software Process Simulation Modeling

Liu¹,

Zhang²,

Dong³

et al. 2023

Preprint

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Background: Software Process Simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement cost and the high demand for domain knowledge. The difficulty of extracting appropriate metrics with real data from process enactment is one of the great challenges. Objective: We aim to provide evidence-based support of the process metrics for software process (simulation) modeling. Method: A systematic literature review was performed by extending our previous review series to draw a comprehensive understanding of the metrics for process modeling following a meta-model of ontology of metrics in SPS. Results: We identified 145 process modeling studies that collectively involve 2130 metrics and classified them using the coding technique. Two diagrams which illustrate the high frequency causal relationships used between metrics are proposed in terms of two hierarchical levels of modeling purposes. The specific metrics of different paradigms are compared, and the main difference is that Discrete-Event Simulation (DES) and Agent-Based Simulation (ABS) can provide more detailed simulations from the perspective of development activities and individual developers whilst System Dynamics (SD) tends to use the mean value as an alternative. We revisited the data issues encountered in SPS data preparing phases, as well as identified the corresponding strategies. Conclusion:The results of this study provide process modelers with an evidence-based reference of the identification and the use of metrics in SPS modeling, and further contribute to the development of the body of knowledge on software metrics in the context of process modeling. Furthermore, this study is not limited to process simulation but can be extended to software process modeling, in general. Taking simulation metrics as standards and references can further motivate and guide software developers to improve the collection, governance, and application of process data in practice.

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Section: Comparison Of Different Modeling Paradigms In Modelingmentioning

confidence: 99%

Metrics for Software Process Simulation Modeling

Liu¹,

Zhang²,

Dong³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…An approach that compares ABS for software processes with SD taking individual factors such as experience and competence of developers into account, is presented by Cherif and Davidsson [108]. They state that SD is easier to configure, because it does not need as much information on the behavior of individuals.…”

Section: Software Process Simulationmentioning

confidence: 99%

Mining Developer Dynamics for Agent-Based Simulation of Software Evolution

Herbold¹

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The steady growth of software in our daily life results in the need for quicker adaption of the software changing usage and requirements. This process is defined as software evolution. Primarily, it is concerned with changes that are responsible for the evolution. The most important contribution to this process results from developers, e.g., by adding code to the repository. This process is highly dynamic as the team constellation as well as the activity of individual developers is always changing. This is especially the case for open-source software (OSS) projects which are analyzed in this thesis because of the free availability.We create and evaluate several models describing software evolution. The main focus of the approach described in this thesis is in the source of the changes, i.e., the developers. Using Agent-based simulation, project managers have the ability to try different scenarios and estimate possible software evolution trends. For example, it is possible to choose a team constellation and evaluate if the chosen team will be able to fix enough bugs using the simulation output. If not, more developers can be added to the simulation. In this case, the developers are agents who create, update, and delete software artifacts and possibly add or fix bugs at the same time. Huge parts of this thesis are dedicated to find suitable simulation parameters and estimate them by mining software repositories to gain a realistic simulation. Questions like the size of the software project, the activity of developers, the number of bugs, and the structure of the software under simulation can be answered. We apply methods from data mining, machine learning and statistics for our work.For the simulation, the behavior of developers is estimated using heuristics gained from analyzing the history of different software projects. The resulting simulation model reflects different developer roles with varying workload. Although, the representation of OSS dynamics was limited. For a fine-grained developer contribution behavior, a state-based probabilistic model (Hidden Markov Model) was trained based on different levels of codebased and communication-based activities. This allows the developers to switch between different of activity. The same procedure is used to summarize the whole project activity with the aim to evaluate whether a project is still active. Therefore, we are interested in finding out how much activity is still performed in inactive projects, since a strict separation is difficult to find, but important for potential users of the project.The results of three case studies show that Agent-based simulation is a promising approach for the prediction of software evolution and that many relations can be described this way. In particular, it turned out that a dynamic developer and project behavior is indispensable for the description of OSS evolution, because otherwise the representation of software processes is too static.During the work on this thesis, several people supported me in many ways. First, I would lik...

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Metrics for software process simulation modeling

Liu,

Zhang,

Dong

et al. 2024

J Software Evolu Process

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Software process simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement cost and the high demand for domain knowledge. The difficulty of extracting appropriate metrics with real data from process enactment is one of the great challenges. We aim to provide evidence‐based support of the process metrics for software process (simulation) modeling. A systematic literature review was performed by extending our previous review series to draw a comprehensive understanding of the metrics for process modeling following our proposed ontology of metrics in SPS. We identify 131 process modeling studies that collectively involve 1975 raw metrics and classified them into 21 categories using the coding technique. We found product and process external metrics are not used frequently in SPS modeling while resource external metrics are widely used. We analyze the causal relationships between metrics. We find that the models exhibit significant diversity, as no pairwise relationship between metrics accounts for more than 10% SPS models. We identify 17 data issues may encounter in measurement and 10 coping strategies. The results of this study provide process modelers with an evidence‐based reference of the identification and the use of metrics in SPS modeling and further contribute to the development of the body of knowledge on software metrics in the context of process modeling. Furthermore, this study is not limited to process simulation but can be extended to software process modeling, in general. Taking simulation metrics as standards and references can further motivate and guide software developers to improve the collection, governance, and application of process data in practice.

show abstract

Software Development Process Simulation: Multi Agent-Based Simulation versus System Dynamics

Cited by 12 publications

References 13 publications

Metrics for Software Process Simulation Modeling

Metrics for Software Process Simulation Modeling

Mining Developer Dynamics for Agent-Based Simulation of Software Evolution

Metrics for software process simulation modeling

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