Thesis Abstract Software process assessments are currently being conducted by organisations using de facto assessment standards such as ISO/IEC 15504, ISO 9001, CMM and BOOTSTRAP. These assessment standards require practical tools and support mechanisms to enable them to be effective and efficient in their execution. This thesis is a study of the functional composition of such automated tools and investigates the viability of creating mappings between the software process models that would allow the assessment data to be translated between models. The result is a model for creating automated assessment tools and a methodology for using data mappings to translate and compare assessment data between software process models in these assessment tools.

Software process modelling has been identified as being a vehicle for understanding development processes, controlling development costs, duration, and achieving product quality. In recent years, software process simulation has been becoming one of the essential techniques for effectively investigating and managing software development processes. Till now, most researches focus on the quantitative aspects of process simulation and modelling. Nevertheless, purely quantitative process modelling requires a very detailed understanding and accurate measurement of the software process, which relies on reliable and precise historical data. When such data are lacking or the quality is dubious, quantitative models have to impose severe constraints that restrict the model's value. Unfortunately, these data are not readily available in most cases, especially in the organisations at low process maturity levels. In addition, software development is a highly complex, human-centred endeavour, which involves many uncertain factors in the course of development process. Facing the inherent uncertainty and contingency, though quantitative modelling employs statistic techniques, its conditional capability and underlying assumptions limit its performance on large scale problems. As the alternatives of quantitative approaches, qualitative modelling can cope with a lack of complete knowledge, and predicts qualitative process behaviours. Furthermore, semi-quantitative modelling offers the capability of handling process uncertainty with limited knowledge, and achieves tradeoff between quantitative and qualitative approaches. However, most previous researches omitted these approaches, and the associated methods and applications are far from developed. The main contribution of this research lies in the pioneering work on the models, methods, and applications of qualitative and semi-quantitative software process modelling and simulation, and their relations with the conventional, quantitative modelling approaches. This dissertation produces its novelty from twofold research. Firstly, it explores methods and techniques to qualitatively and semi-quantitatively model and simulate software processes at different levels, i.e. project, portion of development process, and product evolution. Secondly, Some exclusive applications of these modelling approaches are also developed for aspects of software engineering practice. Moreover, a proposed framework integrates these approaches with typical quantitative paradigms to guide the adoption of process simulation modelling in software organisations. As a comprehensive reflection of state-of-the-art of software process simulation modelling, a systematic review is reported in this dissertation as well.

Project Management is the primary key to successful software development. In 1995 Caper Jones stated that the failure or cancellation rate of large software systems was over 20% in his article on patterns of large software systems. More than two thirds of the projects fail due to improper management of skills, activities, and personnel. One main reason is that software is not a tangible entity and is hard to visualize and hence to monitor. A manager has to be skilled in different CASE tools and technologies to track and manage a software development process successfully. The volume of results produced by these CASE tools is so huge that a high level manager cannot look into all the details. He has to get a high level picture of the project, to know where the project is heading, and if needed, then look into the finer level details by drilling down to locate and correct problems. The objective of this thesis is to build an Advanced Charting System (ACS), which would act as a companion to PAMPA 2 (Project Attribute Monitoring and Prediction Associate) and help a manager visualize the state of a software project over a standard World Wide Web browser. The PAMPA 2 ACS will be responsible for visualizing and tracking of resources, tasks, schedules and milestones of a software project described in the plan. PAMPA 2 ACS will have the ability to depict the status of the project through a variety of graphs and charts. PAMPA 2 ACS implements a novel charting technique called as DOT Chart to track the processes and activities of a software project. PAMPA 2 ACS provides a multilevel view of the project status. PAMPA 2 ACS will be able to track any arbitrary plan starting from a collapsed / concise view of a whole project. This can be further drilled down to the lowest level of detail. The status can be viewed at the project version level, plan and workbreakdown levels, process, sub process, and activity level. Among all the process models, the DOT charts can be applied effectively to spiral process model where each spiral represents a project version.

Software defect detection research typically focuses on individual inspection and testing techniques. However, to be effective in applying defect detection techniques, it is important to recognize when to use inspection techniques and when to use testing techniques. In addition, it is important to know when to deliver a product and use maintenance activities, such as trouble shooting and bug fixing, to address the remaining defects in the software.<p>To be more effective detecting software defects, not only should defect detection techniques be studied and compared, but the entire software defect detection process should be studied to give us a better idea of how it can be conducted, controlled, evaluated and improved.<p>This thesis presents a self-evolving software defect detection process (SEDD) that provides a systematic approach to software defect detection and guides us as to when inspection, testing or maintenance activities are best performed. The approach is self-evolving in that it is continuously improved by assessing the outcome of the defect detection techniques in comparison with historical data.<p>A software architecture and prototype implementation of the approach is also presented along with a case study that was conducted to validate the approach. Initial results of using the self-evolving defect detection approach are promising.

Software defect detection research typically focuses on individual inspection and testing techniques. However, to be effective in applying defect detection techniques, it is important to recognize when to use inspection techniques and when to use testing techniques. In addition, it is important to know when to deliver a product and use maintenance activities, such as trouble shooting and bug fixing, to address the remaining defects in the software.<p>To be more effective detecting software defects, not only should defect detection techniques be studied and compared, but the entire software defect detection process should be studied to give us a better idea of how it can be conducted, controlled, evaluated and improved.<p>This thesis presents a self-evolving software defect detection process (SEDD) that provides a systematic approach to software defect detection and guides us as to when inspection, testing or maintenance activities are best performed. The approach is self-evolving in that it is continuously improved by assessing the outcome of the defect detection techniques in comparison with historical data.<p>A software architecture and prototype implementation of the approach is also presented along with a case study that was conducted to validate the approach. Initial results of using the self-evolving defect detection approach are promising.

The software industry and the information service providers in Taiwan are facing global competition but generally falling behind the international level for their software development ability due to short of capable manpower and lack of software engineering concept. Moreover, the domestic output value of software is relatively low and the domestic market is also too small; companies are struggling for the market and making profit has become difficult. To solve this issue the Software Engineering Institute (SEI) has developed several combinations of the Capability Maturity Model Integrated (CMMI) in recent years. In this study, theories of implementing CMMI in the software industry are discussed. Taking risk management for a software project as example, we have established the structure and guides of process tailoring for projects of various scales. On the process of risk management, we firstly defined the tailoring aspects according to the CMMI Level 3 implement process, and then interviewed the case study company for their eight plans to analyze and induce the possible tailoring guides. Our findings are as follows. 1. Establishing the risk management strategy (1) Making a risk plan, including activities such as risk definition, analysis, reduction, treatment and monitoring. (2) Providing implement methods, judgment rules, execution thresholds, treatment process, or tools for those activities. (3) Producing necessary output, such as data, records, and documents. (4) Setting up timeframe for risk monitoring or re-evaluation, at least once a month. 2. Establishing the tailoring guides for the process (1) Adjustment and change are allowed, depending on the project property. (2) Feedback is necessary for design and implementation. (3) Concrete and practical methods are necessary; the system is not fully reliable. The research model and structure of this study can be further applied to the newly developed SEI models or other large-scale international standards in the future as a reference for the software organizations to improve their software process.

Project Management is the primary key to successful software development. In 1995 Caper Jones stated that the failure or cancellation rate of large software systems was over 20% in his article on patterns of large software systems. More than two thirds of the projects fail due to improper management of skills, activities, and personnel. One main reason is that software is not a tangible entity and is hard to visualize and hence to monitor. A manager has to be skilled in different CASE tools and technologies to track and manage a software development process successfully. The volume of results produced by these CASE tools is so huge that a high level manager cannot look into all the details. He has to get a high level picture of the project, to know where the project is heading, and if needed, then look into the finer level details by drilling down to locate and correct problems. The objective of this thesis is to build an Advanced Charting System (ACS), which would act as a companion to PAMPA 2 (Project Attribute Monitoring and Prediction Associate) and help a manager visualize the state of a software project over a standard World Wide Web browser. The PAMPA 2 ACS will be responsible for visualizing and tracking of resources, tasks, schedules and milestones of a software project described in the plan. PAMPA 2 ACS will have the ability to depict the status of the project through a variety of graphs and charts. PAMPA 2 ACS implements a novel charting technique called as DOT Chart to track the processes and activities of a software project. PAMPA 2 ACS provides a multilevel view of the project status. PAMPA 2 ACS will be able to track any arbitrary plan starting from a collapsed / concise view of a whole project. This can be further drilled down to the lowest level of detail. The status can be viewed at the project version level, plan and workbreakdown levels, process, sub process, and activity level. Among all the process models, the DOT charts can be applied effectively to spiral process model where each spiral represents a project version.

Software process modelling has been identified as being a vehicle for understanding development processes, controlling development costs, duration, and achieving product quality. In recent years, software process simulation has been becoming one of the essential techniques for effectively investigating and managing software development processes. Till now, most researches focus on the quantitative aspects of process simulation and modelling. Nevertheless, purely quantitative process modelling requires a very detailed understanding and accurate measurement of the software process, which relies on reliable and precise historical data. When such data are lacking or the quality is dubious, quantitative models have to impose severe constraints that restrict the model's value. Unfortunately, these data are not readily available in most cases, especially in the organisations at low process maturity levels. In addition, software development is a highly complex, human-centred endeavour, which involves many uncertain factors in the course of development process. Facing the inherent uncertainty and contingency, though quantitative modelling employs statistic techniques, its conditional capability and underlying assumptions limit its performance on large scale problems. As the alternatives of quantitative approaches, qualitative modelling can cope with a lack of complete knowledge, and predicts qualitative process behaviours. Furthermore, semi-quantitative modelling offers the capability of handling process uncertainty with limited knowledge, and achieves tradeoff between quantitative and qualitative approaches. However, most previous researches omitted these approaches, and the associated methods and applications are far from developed. The main contribution of this research lies in the pioneering work on the models, methods, and applications of qualitative and semi-quantitative software process modelling and simulation, and their relations with the conventional, quantitative modelling approaches. This dissertation produces its novelty from twofold research. Firstly, it explores methods and techniques to qualitatively and semi-quantitatively model and simulate software processes at different levels, i.e. project, portion of development process, and product evolution. Secondly, Some exclusive applications of these modelling approaches are also developed for aspects of software engineering practice. Moreover, a proposed framework integrates these approaches with typical quantitative paradigms to guide the adoption of process simulation modelling in software organisations. As a comprehensive reflection of state-of-the-art of software process simulation modelling, a systematic review is reported in this dissertation as well.

Lic-avh. Ronneby : Tekn. högsk.

Made available in DSpace on 2014-06-12T15:53:37Z (GMT). No. of bitstreams: 2 arquivo1924_1.pdf: 3478481 bytes, checksum: a0f58f98b37132402ca52683d62410df (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Fundação de Amparo à Pesquisa do Estado do Amazonas / As empresas de software brasileiras buscam conquistar cada vez mais o mercado nacional e internacional, os quais estão mais competitivos. A estratégia viável é investir no aumento da qualidade e produtividade. O foco desse trabalho é investigar fatores relevantes para mensurar o Return on Investment (ROI) em Melhoria de Processo de Software (MPS). Com o objetivo de propor um framework constituído por fases baseado nos conceitos da ROI Methodology, utilizando indicadores utilizados por David Rico em ROI of SPI e uma seleção de medições utilizadas para MPS. As fases são: Identificação do problema; Diagnóstico detalhado; Estimativa de ROI; Implementação e Encerramento. Para cada fase, baseados no paradigma GQM Goal-Question-Metric foram definidos indicadores de medição para monitorar o FROISPI. As quatro primeiras fases seguem o conceito clássico do PDCA, que para cada solução sugerida de melhoria, analisa seus resultados e se os mesmos forem considerados plenamente satisfatórios, seguirá para a fase de Encerramento, caso contrário o processo cíclico continua até a necessidade de melhoria ser satisfeita. Na fase de Encerramento serão apresentados à alta direção os resultados alcançados com a utilização do FROISPI. O experimento foi executado em três organizações de maturidade bem distintas, mas somente uma organização conseguiu concluir com êxito