DISTANCE: a framework for software measure construction.

In this paper we present a framework for software measurement that is specifically suited to satisfy the measurement needs of empirical software engineering research. The framework offers an approach to measurement that builds upon the easily imagined, detected and visualised concepts of similarity and dissimilarity between software entities. These concepts are used both to model the software attributes of interest and to define the corresponding software measures. Central to the framework is a process model that embeds constructive procedures for attribute modelling and measure construction into a goal-oriented approach to empirical software engineering studies. The underlying measurement theoretic principles of our approach ensure the construct validity of the resulting measures. The approach was tested on a popular suite of object-oriented design measures. We further show that our measure construction method compares favourably to related work.Software;

Critical sucess factors in software process improvement

The aim of the thesis project was to identify Software Process Improvement (SPI) success factors through a deeply literature review. Objectives: • Identify the models of SPI • To identify SPI success factors through literature review. • To identify the most important SPI success factors based on the frequency of occurrence in published research. • To identify the variety of definitions or explanations of the most important SPI success factors in published research

Critical sucess factors in software process improvement

The aim of the thesis project was to identify Software Process Improvement (SPI) success factors through a deeply literature review. Objectives: • Identify the models of SPI • To identify SPI success factors through literature review. • To identify the most important SPI success factors based on the frequency of occurrence in published research. • To identify the variety of definitions or explanations of the most important SPI success factors in published research

Transitioning to a Lean Enterprise: A Guide for Leaders, Volume III, Roadmap Explorations

Volume III of this guide may be used as an in-depth reference source for acquiring deep knowledge about many of the aspects of transitioning to lean. Lean change agents and lean implementation leaders should find this volume especially valuable in preparing their organizations for the lean transformation and in developing and implementing an enterprise level lean implementation plan. The richness and depth of the discussions in this volume should be helpful in charting a course, avoiding pitfalls, and making in-course corrections during implementation. We assume that the reader of Volume III is familiar with the history and general principles of the lean paradigm that are presented in Volume I, Executive Overview. A review of Volume II, Transition to Lean Roadmap may be helpful prior to launching into Volume III. For those readers most heavily involved in the lean transformation, all three volumes should be understood and referenced frequently

How Decision Makers Learn to Choose Organizational Performance Measures

This study, framed by decision making, program theory, and performance measurement theory, explored the knowledge and experience that enable decision makers to identify organizational performance measures. It used a mixed method, exploratory sequential research design to discover the experience, knowledge, and skills (EKS) senior decision makers felt were important in learning to choose organizational performance measures. From the analyzed interviews, a survey was designed to measure the importance of the EKS characteristics. Qualitative analysis identified 55 life, work, or educational experience; knowledge; or skill characteristics and 23 effective measure characteristics. Regression analysis and PCA were used to extract 6 components. One-way ANOVA found no significant differences in these factors between gender groups, age groups, and process complexity levels, but found differences for decision-making tenure. MANOVA found no significant differences by the same dimensions. The limited sample size and high number of variables confounded component extraction. Further research with a suitable sample size is required before findings can be generalized

Software process quality models: a comparative evaluation

Numerous software processes are implemented by software organisations in the production and maintenance of software products. Varying levels of success are observed in their execution, as processes vary in content and quality. A number of quality models for software processes have been published, each of which is intended to encompass the totality of quality factors and issues relevant to a specific notion of process quality. These quality models may be used to develop a new process, measure the quality of existing processes, or guide improvement of existing processes. It is therefore desirable that mechanisms exist to select the model of highest intrinsic quality and greatest relevance. In this thesis, mechanisms are proposed for the comparative evaluation of software process quality models. Case studies are performed in which existing software process quality models are applied to existing software processes. Case study results are used in empirical evaluation of models to augment theoretical evaluation results. Specific recommendations are made for selection of models against typical selection criteria. Assessment is performed of the assessment procedures against defined success criteria. Theoretical evaluation procedures are developed to measure process quality models against defined quality criteria. Measurements are performed of conformance of models to the requirements set for an ideal process quality model, and the relevance of model content to defined stakeholders in software processes. Comparison is also made of the scope and size of models. Empirical evaluation procedures are developed to assess model performance in the context of application to real software processes. These procedures assess the extent to which the results of process measurement using process quality models are observed to differ, and hence the importance of selecting one model in preference to others. Measurement is also performed of the extent of difference in the software processes evaluated in the case studies

Critical Success Factors in Data Mining Projects.

The increasing awareness of data mining technology, along with the attendant increase in the capturing, warehousing, and utilization of historical data to support evidence-based decision making, is leading many organizations to recognize that the effective use of data is the key element in the next generation of client-server enterprise information technology. The concept of data mining is gaining acceptance in business as a means of seeking higher profits and lower costs. To deploy data mining projects successfully, organizations need to know the key factors for successful data mining. Implementing emerging information systems (IS) can be risky if the critical success factors (CSFs) have been researched insufficiently or documented inadequately. While numerous studies have listed the advantages and described the data mining process, there is little research on the success factors of data mining. This dissertation identifies CSFs in data mining projects. Chapter 1 introduces the history of the data mining process and states the problems, purposes, and significances of this dissertation. Chapter 2 reviews the literature, discusses general concepts of data mining and data mining project contexts, and reviews general concepts of CSF methodologies. It also describes the identification process for the various CSFs used to develop the research framework. Chapter 3 describes the research framework and methodology, detailing how the CSFs were identified and validated from more than 1,300 articles published on data mining and related topics. The validated CSFs, organized into a research framework using 7 factors, generate the research questions and hypotheses. Chapter 4 presents analysis and results, along with the chain of evidence for each research question, the quantitative instrument and survey results. In addition, it discusses how the data were collected and analyzed to answer the research questions. Chapter 5 concludes with a summary of the findings, describing assumptions and limitations and suggesting future research

The SMPI model : a stepwise process model to facilitate software measurement process improvement along the measurement paradigms

Software engineering, software measurement, software process engineering, capability, maturityMagdeburg, Univ., Fak. für Informatik, Diss., 2007René Braungarte