Impact of personnel factors on the recovery of delayed software projects: A system dynamics approach

Farshchi, Mostafa; Jusoh, Yusmadi Yah; Murad, Azmi

doi:10.2298/csis110525003f

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Abdel‐Hamid and Madnick (1987) and the constructive cost model (COCOMO) (Boehm, 1981) are the most widely known software models for PM. The COCOMO model (or COCOMO 81) was further supported by Madachy (1996) to simulate the effect of an inspection‐based software life cycle process, in line with tasks, errors and effects (Farshchi et al, 2012; Lee & Miller, 2004), technical issues (Rus et al, 1999) and risk (Lopes et al, 2015). This COCOMO model was first published in the book “Software Engineering Economics” by Boehm (1981).…”

Section: Resultsmentioning

confidence: 99%

Project management and system dynamics modelling: Time to connect with innovation and sustainability

et al. 2023

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This paper argues that there is a requirement to connect project management (PM) with innovation and sustainability to address new societal needs. Societal demands have facilitated further complexity and uncertainty within the PM discipline and have led to both theoretical and practical evolutions. Key evolutions are identified, which suggests that PM is becoming more holistic and interdisciplinary in nature so it can successfully deal with this complexity and uncertainty. Furthermore, holistic thinking naturally connects with systemic thinking, which justifies the selection of system dynamics (SD) to support PM decision‐making. An extensive critical review of SD modelling with respect to identified evolutions within the PM discipline is undertaken. The contribution of this paper connects innovation and sustainability to the PM discipline by the initial development of a causal loop diagram (CLD).

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Section: Resultsmentioning

confidence: 99%

Project management and system dynamics modelling: Time to connect with innovation and sustainability

et al. 2023

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“…Narrative explanation [30], [33], [58], [82]- [86] Literature review [31], [87]- [89] Literature review, interview [32], [35], [ Exploring which variables to incorporate into the model and how they are causally related to others is a task that should be linked to identifying instruments to measure them. Doing this allows, in the model's practical application, the collection of data to be consistent with the purpose of the analysis.…”

Section: Methods Referencesmentioning

confidence: 99%

Characterizing Social and Human Factors in Software Development Team Productivity: A System Dynamics Approach

Restrepo-Tamayo,

Gasca-Hurtado,

Valencia-Calvo

2024

IEEE Access

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Software development projects demand high levels of interaction between work team members. This way, management and decision-making must be supported by analyzing the complex dynamics generated through individual interactions to complete the projects. This complexity can be addressed using system dynamics. This modeling approach studies how the structures and relationships between variables in a system interact to generate behaviors over time. It is used to understand and analyze complex systems and make informed decisions. The first step in modeling is articulating the problem. This step defines the key variables that will be included in the model. Still, the lack of a standardized procedure to select, measure, and propose causal relationships is evident. Subjectivity is often appealed to, but this could lead to inaccurate models and biased results. The challenge intensifies when it comes to qualitative variables. This study introduces a formal methodology to characterize such variables, addressing a gap in the existing literature. The use of systematic mapping and a survey-based study is proposed. The methodology is applied to characterize three social and human factors that influence the productivity of software development teams: communication, leadership, and teamwork. The results captured primary experimental research's proven definitions, measurement mechanisms, and causal relationships. This formalized approach not only fills a significant gap in system dynamics but also lays a foundation for expanding its scope to encompass additional variables. As such, it represents a substantial methodological contribution to the field.

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“…The difference between these two methods is that the former is chosen when a related and validated model is available but the actual process data are hard to access, 53 while the latter is used to check whether the model's outputs and the actual data are similar when actual (empirical) data are available. 60 Both comparison with actual data and experimental case need empirical process data to verify a model. However, the purposes of them in modeling practice are different.…”

Section: Rq2: What Methods Are Used In Vandv Of Spsms?mentioning

confidence: 99%

“…For example, comparison with other model and comparison with actual data can both be used to check the model's outputs. The difference between these two methods is that the former is chosen when a related and validated model is available but the actual process data are hard to access, 53 while the latter is used to check whether the model's outputs and the actual data are similar when actual (empirical) data are available 60 …”

Section: Findings and Mappingmentioning

confidence: 99%

Verification and validation of software process simulation models: A systematic mapping study

Li,

Zhang,

Liu

et al. 2023

J Software Evolu Process

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Software process simulation models (SPSMs) that are based on descriptive process models offer the executability that can demonstrate dynamic changes of software processes over time. Verification and validation (V&V) is critical in SPSMs for guaranteeing the quality and reliability of models. V&V of dynamic software process models is more complex and challenging than for static software process models. This work systematically summarizes and maps V&V studies in SPSM to provide guidelines for future research and practice. Specifically, this study aims at identifying the focus of research on V&V, the methods used for V&V, and how to implement V&V of SPSMs in software engineering research. We conducted a systematic mapping study on studies of SPSMs that report on their V&V activities. Under the guidance of a V&V meta‐model for SPSMs, we study four research questions about V&V process. We identified 107 primary studies from a pool of 313 papers on SPSMs until 2021. There are two main results of our study. The first one presents the relationship between quality aspects of SPSMs and the V&V methods to assure them. The second result reveals the relationships among the modeling process, three modeling steps, five quality aspects, and 10 V&V methods. Generally, researchers do not pay sufficient attention to V&V, as 65.8% ( ) failed to mention or elaborate on their V&V process. We systematically summarize and map the state‐of‐the‐art V&V research in software process modeling field to support modelers' practice and improve their V&V process.

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Impact of personnel factors on the recovery of delayed software projects: A system dynamics approach

Cited by 8 publications

References 31 publications

Project management and system dynamics modelling: Time to connect with innovation and sustainability

Project management and system dynamics modelling: Time to connect with innovation and sustainability

Characterizing Social and Human Factors in Software Development Team Productivity: A System Dynamics Approach

Verification and validation of software process simulation models: A systematic mapping study

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