Approaches to Process Performance Modeling: A Summary from the SEI Series of Workshops on CMMI High Maturity Measurement and Analysis

Stoddard, II; W., Goldenson Robert; Dennis, R.

doi:10.21236/ada515830

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…As noted in the 2008 high maturity survey report, similar results were evident in two previous surveys in the series [1] [2]. 3 Based on appraised organizations across the full spectrum of CMMI-based maturity levels, both studies provide evidence about the usefulness and role of measurement and analysis capabilities to improve performance outcomes.…”

Section: The Studysupporting

confidence: 64%

“…4 By process performance modeling, we mean various analytical methods that enable the construction and effective use of process performance models and baselines. As was true in the earlier survey, several questions asked about the -healthy ingredients‖ of a CMMIbased process performance model [3]. A list of what the SEI considers healthy ingredients can be found on page 23.…”

Section: The Studymentioning

confidence: 98%

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Performance Effects of Measurement and Analysis: Perspectives from CMMI High Maturity Organizations and Appraisers

McCurley¹,

Goldenson²

2010

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ixii | CMU/SEI-2010-TR-022iii | CMU/SEI-2010-TR-022 List of Tables Table 1: Relationships between individual items about the of use of healthy process performance model ingredients and overall value 49 Table 2: Relationships between individual items about the of use of statistical methods for process performance modeling and overall value 52 Table 3: 2009 -Relationships between the use of automated method to support measurement related activities and overall value attributed to process performance modeling 53 Table 4: Relationships between data quality and integrity activities and overall value attributed to process performance modeling: 54 Table 5: Relationships between modelers' understanding of the intent of CMMI process performance baselines and models and overall value attributed to the model outcomes56 Table 6: Relationship between involvement by individual stakeholder roles and overall value attributed to process performance models 58 Table 7: Relationship between quality of training available for different practitioner roles and overall value attributed to process performance models 60 Table 8: Relationship between technical challenges that projects sometimes face and overall value attributed to process performance models 62 Table 9: Relationships between obstacles to measurement and analysis and overall value of process performance model outcomes 63 Table 10: Relationships between barriers and facilitators of process performance modeling and overall value of the model outcomes 65 Table 11: List of FiguresComparison of relationships between emphasis on healthy process performance model ingredients with achievement of target goals and overall value of process performance model outcomes 68 Table 12:Comparison of relationships between use of healthy process performance model ingredients with achievement of target goals and overall value of process performance model outcomes 69 Table 13:Comparison of relationships between use of statistical methods with achievement of target goals and overall value of process performance model outcomes 71 Table 14:Comparison of relationships between use of simulation and other optimization methods with achievement of target goals and overall value of process performance model outcomes 74 AbstractThis report describes results from two recent surveys conducted by the Software Engineering Institute (SEI) to collect information about the measurement and analysis activities of software systems development organizations. Representatives of organizations appraised at maturity levels 4 and 5 completed the survey in 2008. Using a variant of the same questionnaire in 2009, certified high maturity lead appraisers described the organizations that they had most recently coached or appraised for the achievement of similar high maturity levels. The replies to both surveys were generally consistent even though the two groups are often thought to be quite different. The results of the surveys suggest that the organizations understood and used CMMI-based process performance model...

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Section: The Studysupporting

confidence: 64%

Section: The Studymentioning

confidence: 98%

Performance Effects of Measurement and Analysis: Perspectives from CMMI High Maturity Organizations and Appraisers

McCurley¹,

Goldenson²

2010

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“…For example, in our experience, the targeted organizations that were solicited for the current survey tend to have more programs that regularly use advanced measurement practices to inform their SE practices. Other studies show that such practices often are accompanied by better project performance and product quality outcomes , Stoddard 2010, McCurley 2010.…”

Section: Se Proces Areamentioning

confidence: 99%

The Business Case for Systems Engineering Study: Results of the Systems Engineering Effectiveness Survey

Elm¹,

Goldenson

2012

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“…Performance objectives guide to what extent efforts should be made in smooth execution of process (Jung & Goldenson, 2009). When process performance evaluation is desired, then considerable substances are number of employees required in order to execute a process, available resource pool (skill set level), collaboration time cycle with associated departments, time required for research and development and change in time cycle due to process variation (Cao & Hoffman, 2011;Kumar, 2012;Stoddard, 2007;Stoddard & Goldenson, 2010;Tan, Shen & Zhao, 2007; http://www.omg.org/technology/documents/formal/uml.htm).…”

Section: Performance Evaluation Of Processmentioning

confidence: 99%

Performance analysis of software maintenance process using stochastic Petri nets

Nabeel¹,

Anwar

Ahsan

2018

GJCS

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Software maintenance is a time taking activity in the real world. Execution time of software maintenance process may get increased due to interdepartmental communication, thus, increasing the cost and decreasing the performance of the process. We suggested performance evaluation of software maintenance process through the transformation of activity diagram into generalised stochastic Petri nets (GSPN). For this study, execution time and cost of maintenance process are defined as performance measures, and the role-based approach is used to understand the flow of software maintenance activities in a software organisation. Activity diagram is constructed to be transformed into GSPN. We used PIPE2 to analyse the GSPN. PIPE2 calculates average number of tokens on a place in the GSPN, throughput of timed transition and state space analysis. State space involves calculation of the reachability of the GSPN net that shows whether a GSPN holds the property of safeness, boundness and is deadlock free. Keywords: Generalised stochastic Petri nets (GSPN), GSPN analysis, performance evaluation, performance modelling, software maintenance process, stochastic Petri nets.

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Approaches to Process Performance Modeling: A Summary from the SEI Series of Workshops on CMMI High Maturity Measurement and Analysis

Cited by 3 publications

References 6 publications

Performance Effects of Measurement and Analysis: Perspectives from CMMI High Maturity Organizations and Appraisers

Performance Effects of Measurement and Analysis: Perspectives from CMMI High Maturity Organizations and Appraisers

The Business Case for Systems Engineering Study: Results of the Systems Engineering Effectiveness Survey

Performance analysis of software maintenance process using stochastic Petri nets

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