Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.

System design and the cost of architectural complexity

Software project complexity is a subject that has not received detailed attention. The purpose of this paper is to present a systematic way for studying and modeling software project complexity. The proposed model is based on the widely known and accepted Project Management Body of Knowledge and it uses a typology for modeling complexity based on complexity of faith, fact and interaction.

Measuring the Complexity of Software Projects

Recent researches shows that software projects fail to meet their requirements in terms of time delay, cost overrun and quality restrictions. It is widely accepted that among the main reasons for these failures is the increased complexity of modern software projects arising from their special characteristics in comparison with projects from other domains. Software projects are affected of rapidly technological changes, are immaterial and supple. Current studies measure software project complexity by measuring either the software project product based on its attributes such as size, quality, reliability or the characteristics of software project process using attributes such as performance, stability, improvement. According to our approach these methods are not adequate as they do not focus in the software project development management process. This study, propose the managing of complexity in software projects by applying project management techniques, based on PMBOK, in software project development process and focuses on communication management. Communication management is among the most important knowledge areas in project management. Communication complexity sources that identified are project stakeholders’ properties, project environment, project communication structure, communication properties, physical and psychological barriers. A discussion exploring how these affects project communication performance is taken place. Finally for each complexity source a set of quantitative metrics and appropriate evaluation method are proposed to measure communication complexity.

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Measuring communication complexity in projects

Accurate and reliable effort estimation is still one of the most challenging processes in software engineering. There have been numbers of attempts to develop cost estimation models. However, the evaluation of model accuracy and reliability of those models have gained interest in the last decade. A model can be finely tuned according to specific data, but the issue remains there is the selection of the most appropriate model. A model predictive accuracy is determined by the difference of the various accuracy measures. The one with minimum relative error is considered to be the best fit. The model predictive accuracy is needed to be statistically significant in order to be the best fit. This practice evolved into model evaluation. Models predictive accuracy indicators need to be statistically tested before taking a decision to use a model for estimation. The aim of this thesis is to statistically evaluate well known effort estimation models according to their predictive accuracy indicators using two new approaches; bootstrap confidence intervals and permutation tests. In this thesis, the significance of the difference between various accuracy indicators were empirically tested on the projects obtained from the International Software Benchmarking Standard Group (ISBSG) data set. We selected projects of Un-Adjusted Function Points (UFP) of quality A. Then, the techniques; Analysis Of Variance ANOVA and regression to form Least Square (LS) set and Estimation by Analogy (EbA) set were used. Step wise ANOVA was used to form parametric model. K-NN algorithm was employed in order to obtain analogue projects for effort estimation use in EbA. It was found that the estimation reliability increased with the pre-processing of the data statistically, moreover the significance of the accuracy indicators were not only tested statistically but also with the help of more complex inferential statistical methods. The decision of selecting non-parametric methodology (EbA) for generating project estimates in not by chance but statistically proved.

The Evaluation of Well-known Effort Estimation Models based on Predictive Accuracy Indicators

Software project complexity is a subject that has not received detailed attention The purpose of this chapter is to present a systematic way for studying and modeling software project complexity The proposed model is based on the widely known and accepted Project Management Body of Knowledge and it uses a typology for modeling complexity based on complexity of faith, fact, and interaction

Classification of Software Projects’ Complexity

Software Measurement and Estimation

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Software Measurement and Estimation