The Social Psychology of Organizations.

This paper presents the results of a study in which we empirically investigated the suite of object-oriented (OO) design metrics introduced in (Chidamber and Kemerer, 1994). More specifically, our goal is to assess these metrics as predictors of fault-prone classes and, therefore, determine whether they can be used as early quality indicators. This study is complementary to the work described in (Li and Henry, 1993) where the same suite of metrics had been used to assess frequencies of maintenance changes to classes. To perform our validation accurately, we collected data on the development of eight medium-sized information management systems based on identical requirements. All eight projects were developed using a sequential life cycle model, a well-known OO analysis/design method and the C++ programming language. Based on empirical and quantitative analysis, the advantages and drawbacks of these OO metrics are discussed. Several of Chidamber and Kemerer's OO metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle. Also, on our data set, they are better predictors than "traditional" code metrics, which can only be collected at a later phase of the software development processes.

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A validation of object-oriented design metrics as quality indicators

Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. We describe an alternative approach to estimation based upon the use of analogies. The underlying principle is to characterize projects in terms of features (for example, the number of interfaces, the development method or the size of the functional requirements document). Completed projects are stored and then the problem becomes one of finding the most similar projects to the one for which a prediction is required. Similarity is defined as Euclidean distance in n-dimensional space where n is the number of project features. Each dimension is standardized so all dimensions have equal weight. The known effort values of the nearest neighbors to the new project are then used as the basis for the prediction. The process is automated using a PC-based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From this work we argue that estimation by analogy is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques.

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Estimating software project effort using analogies

Practitioners have expressed concern over their inability to accurately estimate costs associated with software development. This concern has become even more pressing as costs associated with development continue to increase. As a result, considerable research attention is now directed at gaining a better understanding of the software-development process as well as constructing and evaluating software cost estimating tools. This paper evaluates four of the most popular algorithmic models used to estimate software costs (SLIM, COCOMO, Function Points, and ESTIMACS). Data on 15 large completed business data-processing projects were collected and used to test the accuracy of the models' ex post effort estimation. One important result was that Albrecht's Function Points effort estimation model was validated by the independent data provided in this study [3]. The models not developed in business data-processing environments showed significant need for calibration. As models of the software-development process, all of the models tested failed to sufficiently reflect the underlying factors affecting productivity. Further research will be required to develop understanding in this area.

An empirical validation of software cost estimation models

This paper aims to provide a basis for the improvement of software-estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A Web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) increase the breadth of the search for relevant studies, 2) search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) conduct more studies on estimation methods commonly used by the software industry, and 4) increase the awareness of how properties of the data sets impact the results when evaluating estimation methods

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A Systematic Review of Software Development Cost Estimation Studies

Software engineering metrics and models

Curriculum 68: Recommendations for academic programs in computer science: a report of the ACM curriculum committee on computer science

The Curriculum Committee on Computer Science (C3S) of the Association for Computing Machinery has been considering curriculum problems for approximately three years. During the early part of this period, a number of informal sessions were held with computer people at various national meetings. In the latter part of this three-year period, the Committee has been formally organized and has made a definite effort to arrive at a concrete suggestions for a curriculum.

An undergraduate program in computer science—preliminary recommendations

Publisher Summary This chapter is concerned about effort and cost estimation models which are appropriate for software project development. Project development is meant to include life cycle phases from project design, through system integration, to testing and software delivery. It discusses and evaluates several models for software effort estimation and performance of these models is compared on sets of projects for which some information is available. The models are categorized into (1) historical-experiential models, (2) statistically-based models, (3) theoretically-based models, and (4) composite models. There is some hope that effort and cost models so restricted can be developed which are transportable from one organization to another. The chapter concludes that further experimentation, the gathering of more data, and the combining and enhancing of models will be necessary in order to allow computer scientists to explain and better control the software development process.

Software Effort Estimation and Productivity

When we enter the twenty-first century, computers will have influenced our lives more than any other technology known to civilization. The need for knowledge about computers, computer technology, and the theoretical aspects of computers has grown exponentially. Since its founding in 1947, the Association for Computing Machinery has attempted to address this need for computer knowledge.

The study of computers is an endeavor demanding careful, thorough, and organized development. Recognizing the need for a comprehensive model curriculum for the growing number of institutions offering computer science courses, ACM, in the mid-sixties, established the Curriculum Committee on Computer Science to make recommendations and provide guidelines to institutions. Chaired by Dr. William Atchison of the University of Maryland, this dedicated group of forward-thinking people published preliminary recommendations in September 1965. With financial assistance from the National Science Foundation, they published their final report, "Recommendations for Academic Programs in Computer Science," in Communications of the ACM in March 1968. This curriculum, known as "Curriculum '68," formed the basis of formal computer science study in colleges and universities for the next ten years.

S. D. Conte

Papers

Software engineering metrics and models

Curriculum 68: Recommendations for academic programs in computer science: a report of the ACM curriculum committee on computer science

An undergraduate program in computer science—preliminary recommendations

Software Effort Estimation and Productivity

ACM Recommended Curricula for Computer Science and Information Processing Programs in Colleges and Universities, 1968-1981