Software project management using PROMPT: A hybrid metrics, modeling and utility framework

Raffo, David

doi:10.1016/j.infsof.2005.09.004

Cited by 20 publications

(21 citation statements)

References 6 publications

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“…Year [62] used discrete event simulation using stochastic data for each simulation run, i.e. the data input for the calibration of the model was drawn from probabilities.…”

Section: Refmentioning

confidence: 99%

“…The event-based models using queuing/discrete event or statebased simulation [31,61,62] all applied the simulation models in an industrial context and provided positive results, such as the support of simulation helps to achieve higher CMM levels [31], aids in getting a buy-in for change initiatives [31], aid in understandability [31], and accurate prediction of past performance [62]. The study presented in [62] reported that predictive accuracy could be improved when continuously updating the simulation model by linking it to a corporate data repository for collected measures.…”

Section: Approach Advantages Drawbacks Evaluation Summarymentioning

confidence: 99%

“…The study presented in [62] reported that predictive accuracy could be improved when continuously updating the simulation model by linking it to a corporate data repository for collected measures. However, the accuracy reported in the studies was not quantified.…”

Section: Approach Advantages Drawbacks Evaluation Summarymentioning

confidence: 99%

See 2 more Smart Citations

Measuring and predicting software productivity: A systematic map and review

Petersen¹

2011

Information and Software Technology

109

107

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“…Year [62] used discrete event simulation using stochastic data for each simulation run, i.e. the data input for the calibration of the model was drawn from probabilities.…”

Section: Refmentioning

confidence: 99%

Section: Approach Advantages Drawbacks Evaluation Summarymentioning

confidence: 99%

See 1 more Smart Citation

Measuring and predicting software productivity: A systematic map and review

Petersen¹

2011

Information and Software Technology

109

107

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“…The management of packaged software projects has received the attention of researchers who traditionally focused on studying the software production project in software houses. From this empirical angle, studies focused on project management techniques and practices (Kraul and Steeler, 1995;Raffo, 2005), teams composition and structure (Carmel and Sawyer, 1998;Dube, 1998;Sawyer, 2000;Sawyer, 2004), sources of knowledge (Segelod and Jordan, 2004), threats (White, 2006), risk management (Wallace, Keilb and Rai, 2004), control of time and cost, and project performance and success (Procaccino and Verner, 2006).…”

Section: Packaged Software Project Managementmentioning

confidence: 99%

Rethinking IS project boundaries in practice: A multiple-projects perspective

Elbanna

2010

The Journal of Strategic Information Systems

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Information Systems research and prescriptive IS project management methodologies are dominated by a perspective on single projects that treats the unit of analysis as a lonely phenomenon with strictly defined boundaries. This study questions this assumption by exploring how the taken for granted project's boundaries are defined in practice. It investigates a case study of an ERP implementation project in an international organization. The findings show the busy multiple-projects platform of contemporary organizations that ERP project cannot be isolated from. They also reveal that project management boundaries are continually crossed and that project's boundaries in practice are malleable and changeable. They are defined through negotiations with other projects and programs where what is inside or outside a project is subject to change according to the outcomes of such negotiations. A flatter view of project organizing could facilitate such an interaction. The implications for IS project management research and practice are discussed.

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“…Raffo (2005) and Garousi et al (2009) mention model validity in a similar way. They take several perspectives into account, such as model structure, supporting data, input parameters and scenarios, and simulation output.…”

Section: Simulation Results and Conclusionmentioning

confidence: 81%

Experimentation with dynamic simulation models in software engineering: planning and reporting guidelines

França

Travassos

2015

Empir Software Eng

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Simulation-based studies (SBS) have become an interesting investigation approach for Software Engineering (SE). However, the reports on experiments with dynamic simulation models found in the technical literature lack relevant information, hampering the full understanding of the procedures and results reported, as well as their replicability. Apart from the limitations on the length in conferences and journal papers, some of the relevant information seems to be missing due to methodological issues not considered when conducting such studies. This is the case of missing research questions and goals, lack of evidence regarding the dynamic simulation model validity, poorly designed simulation experiments, amongst others. Based on findings from a previous quasi-systematic literature review, we propose a set of reporting guidelines for SBS with dynamic models in the context of SE aiming at providing guidance on which information the report should contain. Furthermore, these guidelines were evolved to support SBS planning by identifying potential threats to simulation study validity and in making recommendations to avoid them, through qualitative analysis and external evaluation. Finally, we conducted different evaluations regarding both the reporting and planning guidelines, apart from using them to support the planning of a SBS as regards software evolution. A set of 33 reporting and planning guidelines for different stages of the simulation lifecycle and focused on the experimentation with dynamic simulation models have been put together. The first assessments point to a comprehensive set of guidelines, supporting a comprehensive preparation and review of the plans and reports from the studies, apart from the planning of a SBS focused on software evolution, potentially reducing the threats to the experimentation with the validity of dynamic simulation models. The 33 guidelines cannot be understood as separate groups for reporting and planning as they overlap in many aspects. The Empir Software Eng main goal is to use the guidelines to support the planning of a simulation-based study with dynamic models so that experimenters may identify potential threats to validity and produce relevant information for a complete simulation experiment report in advance. Despite their initial contribution to increase the validity of SBS, the reporting and planning of simulationbased experiments with dynamic models still has to be discussed and improved in SE. Therefore, additional assessments of this set of guidelines are needed to strengthen the confidence in their completeness and usefulness.

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Software project management using PROMPT: A hybrid metrics, modeling and utility framework

Cited by 20 publications

References 6 publications

Measuring and predicting software productivity: A systematic map and review

Measuring and predicting software productivity: A systematic map and review

Rethinking IS project boundaries in practice: A multiple-projects perspective

Experimentation with dynamic simulation models in software engineering: planning and reporting guidelines

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