Victor R. Basili

Bio: Victor R. Basili is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Software development & Software construction. The author has an hindex of 78, co-authored 413 publications receiving 28737 citations. Previous affiliations of Victor R. Basili include King Abdulaziz University & University of California, Irvine.

A validation of object-oriented design metrics as quality indicators

Abstract: 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.

The Goal Question Metric Approach

Abstract: As with any engineering discipline, software development requires a measurement mechanism for feedback and evaluation. Measurement is a mechanism for creating a corporate memory and an aid in answering a variety of questions associated with the enactment of any software process. It helps support project planning (e.g., How much will a new project cost?); it allows us to determine the strengths and weaknesses of the current processes and products (e.g., What is the frequency of certain types of errors?); it provides a rationale for adopting/refining techniques (e.g., What is the impact of the technique XX on the productivity of the projects?); it allows us to evaluate the quality of specific processes and products (e.g., What is the defect density in a specific system after deployment?). Measurement also helps, during the course of a project, to assess its progress, to take corrective action based on this assessment, and to evaluate the impact of such action.

The TAME project: towards improvement-oriented software environments

Abstract: Experience from a dozen years of analyzing software engineering processes and products is summarized as a set of software engineering and measurement principles that argue for software engineering process models that integrate sound planning and analysis into the construction process. In the TAME (Tailoring A Measurement Environment) project at the University of Maryland, such an improvement-oriented software engineering process model was developed that uses the goal/question/metric paradigm to integrate the constructive and analytic aspects of software development. The model provides a mechanism for formalizing the characterization and planning tasks, controlling and improving projects based on quantitative analysis, learning in a deeper and more systematic way about the software process and product, and feeding the appropriate experience back into the current and future projects. The TAME system is an instantiation of the TAME software engineering process model as an ISEE (integrated software engineering environment). The first in a series of TAME system prototypes has been developed. An assessment of experience with this first limited prototype is presented including a reassessment of its initial architecture. >

Iterative and incremental developments. a brief history

Abstract: Although many view iterative and incremental development as a modern practice, its application dates as far back as the mid-1950s. Prominent software-engineering thought leaders from each succeeding decade supported IID practices, and many large projects used them successfully. These practices may have differed in their details, but all had a common theme-to avoid a single-pass sequential, document-driven, gated-step approach.

A Methodology for Collecting Valid Software Engineering Data

Abstract: An effective data collection method for evaluating software development methodologies and for studying the software development process is described. The method uses goal-directed data collection to evaluate methodologies with respect to the claims made for them. Such claims are used as a basis for defining the goals of the data collection, establishing a list of questions of interest to be answered by data analysis, defining a set of data categorization schemes, and designing a data collection form. The data to be collected are based on the changes made to the software during development, and are obtained when the changes are made. To ensure accuracy of the data, validation is performed concurrently with software development and data collection. Validation is based on interviews with those people supplying the data. Results from using the methodology show that data validation is a necessary part of change data collection. Without it, as much as 50 percent of the data may be erroneous. Feasibility of the data collection methodology was demonstrated by applying it to five different projects in two different environments. The application showed that the methodology was both feasible and useful.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Design science in information systems research

Abstract: Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science The behavioral-science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology Our objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research In the design-science paradigm, knowledge and understanding of a problem domain and its solution are achieved in the building and application of the designed artifact Three recent exemplars in the research literature are used to demonstrate the application of these guidelines We conclude with an analysis of the challenges of performing high-quality design-science research in the context of the broader IS community

Out of the Crisis

Abstract: According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.