Cost management forms a major discipline in delivering construction projects of different sizes and complexity. Traditional cost management systems are mostly based on principles enacted several decades ago. A notable feature of these traditional cost management systems is that key information required for critical decisions is usually produced too late, and is often too aggregated and configured in a form that is not amenable to the requirements for current project management practice. Other problems associated with traditional cost systems relate to inadequacies in estimating and cost control processes and particularly the lack of integration of cost management across the whole project. The lack of integration means measurements provided by traditional cost systems do not sufficiently align with the goals and objectives set for the project. To address these inherent weaknesses in the current practice of cost management, a number of studies have argued for an integrated alternative that better responds to the information demand and decision making need to be developed. The thesis presents the development of a solution to such an integrated cost management system. The developed solution addresses the gaps of the traditional option by integrating the stages making up the whole life cycle of the project to enable professionals gain an appreciation of the ramifications of any early decisions made. The investigation conducted to support the development of the integrated cost management system and the applied model addresses user requirements and determination of the system boundary conditions for efficacious use by key decision makers. The new cost management system developed achieves a linkage of the planning and control stages into one, with a continuous stream of cost management information in both stages. The integration ensures that cost information is more relevant to the circumstances of the modern project manager.

The objective of this research is to empirically assess the value and validity of a multi-organization data set in the building of prediction models for several ‘local’ software organizations; that is, smaller organizations that might have a few project records but that are interested in improving their ability to accurately predict software project effort. Evidence to date in the research literature is mixed, due not to problems with the underlying research ideas but with limitations in the analytical processes employed: • the majority of previous studies have used only a single organization as the ‘local’ sample, introducing the potential for bias • the degree to which the conclusions of these studies might apply more generally is unable to be determined because of a lack of transparency in the data analysis processes used. It is the aim of this research to provide a more robust and visible test of the utility of the largest multi-organization data set currently available – that from the ISBSG – in terms of enabling smaller-scale organizations to build relevant and accurate models for project-level effort prediction. Stepwise regression is employed to enable the construction of ‘local’, ‘global’ and ‘refined global’ models of effort that are then validated against actual project data from eight organizations. The results indicate that local data, that is, data collected for a single organization, is almost always more effective as a basis for the construction of a predictive model than data sourced from a global repository. That said, the accuracy of the models produced from the global data set, while worse than that achieved with local data, may be sufficiently accurate in the absence of reliable local data – an issue that could be investigated in future research. The study concludes with recommendations for both software engineering practice – in setting out a more dynamic scenario for the management of software development – and research – in terms of implications for the collection and analysis of software engineering data.

Programinės įrangos kūrimo projektų dokumentacijos kūrimas dažnai vykdomas neefektyviai – dokumentai kuriami lėtai, esama dokumentavimo metodika būna perteklinė, nėra vieningos dokumentacijos kūrimo strategijos. Vienas iš būdų paspartinti dokumentų ruošimo procesą – panaudoti šiame darbe suformuluotus nurodymus bei principus. Taikant sukurtus dokumentų šablonus, dokumentaciją galima generuoti tiesiai iš projektavimo įrankio. Šiuose šablonuose realizuotas papildomas funkcionalumas – projekto kūrimo ir testavimo darbų apimčių skaičiavimas bei elementų ryšių vientisumo nustatymas, o tai svarbu vertinant projekto kokybę ir planuojant kūrimo darbus. Sukūrus projektą, dokumentacija automatiškai sugeneruojama panaudojant projektavimo įrankio, pavyzdžiui, MagicDraw UML, teikiamas dokumentų generavimo priemones, todėl nereikia atskirai kurti dokumentacijos teksto redagavimo priemonėmis. Sukurtąjį dokumento šabloną galima pakartotinai panaudoti kituose projektuose. / Creation of documentation usually is inefficient - documents are being created slowly, the methodology of documentation is usually redundant, there is no common strategy for this. The paper proposes a way to boost the process of creating documentation by using documentation templates that give an opportunity to generate documentation directly from a CASE tool. The created template implements additional functionality - the estimation of a size of programming and testing tasks of a project and tracing the dependencies between project's elements. The implemented template of a document may be applied in more than one project.

The objective of this research is to empirically assess the value and validity of a multi-organization data set in the building of prediction models for several ‘local’ software organizations; that is, smaller organizations that might have a few project records but that are interested in improving their ability to accurately predict software project effort. Evidence to date in the research literature is mixed, due not to problems with the underlying research ideas but with limitations in the analytical processes employed: • the majority of previous studies have used only a single organization as the ‘local’ sample, introducing the potential for bias • the degree to which the conclusions of these studies might apply more generally is unable to be determined because of a lack of transparency in the data analysis processes used. It is the aim of this research to provide a more robust and visible test of the utility of the largest multi-organization data set currently available – that from the ISBSG – in terms of enabling smaller-scale organizations to build relevant and accurate models for project-level effort prediction. Stepwise regression is employed to enable the construction of ‘local’, ‘global’ and ‘refined global’ models of effort that are then validated against actual project data from eight organizations. The results indicate that local data, that is, data collected for a single organization, is almost always more effective as a basis for the construction of a predictive model than data sourced from a global repository. That said, the accuracy of the models produced from the global data set, while worse than that achieved with local data, may be sufficiently accurate in the absence of reliable local data – an issue that could be investigated in future research. The study concludes with recommendations for both software engineering practice – in setting out a more dynamic scenario for the management of software development – and research – in terms of implications for the collection and analysis of software engineering data.