This paper presents a survey of computer algorithms used for the detection of student plagiarism. A summary of several algo-rithms is provided. Common features of the different plagiarism detection algorithms are described. Ethical and administrative issues involving detected plagiarism are discussed.

Intellectual control over software development projects requires the existence of an integrated set of explicit models of the products to be developed, the processes used to develop them, the resources needed, and the productivity and quality aspects involved. In recent years the development of languages, methods and tools for modeling software processes, analyzing and enacting them has become a major emphasis of software engineering research. The majority of current process research concentrates on prescriptive modeling of small, completely formalizable processes and their execution entirely on computers. This research direction has produced process modeling languages suitable for machine rather than human consumption. The MVP project, launched at the University of Maryland and continued at Universitt Kaiserslautern, emphasizes building descriptive models of large, real-world processes and their use by humans and computers for the purpose of understanding, analyzing, guiding and improving software development projects. The language MVP-L has been developed with these purposes in mind. In this paper, we motivate the need for MVP-L, introduce the prototype language, and demonstrate its uses. We assume that further improvements to our language will be triggered by lessons learned from applications and experiments.

We present an empirical investigation of the modeling techniques for identifying fault-prone software components early in the software life cycle. Using software complexity measures, the techniques build models which classify components as likely to contain faults or not. The modeling techniques applied in this study cover the main classification paradigms, including principal component analysis, discriminant analysis, logistic regression, logical classification models, layered neural networks, and holographic networks. Experimental results are obtained from 27 academic software projects. We evaluate the models with respect to four criteria: predictive validity, misclassification rate, achieved quality, and verification cost. A surprising result is that no model is able to discriminate between components with faults and components without faults.

Intellectual control over software development projects requires the existence of an integrated set of explicit models of the products to be developed, the processes used to develop them, and the productivity and quality aspects involved. Processes especially are still based mostly on implicit models. In recent years the development of languages, methods and tools for modeling software processes, analyzing and enacting them has become a major emphasis of software engineering research. The majority of current process research projects concentrate on the prescriptive modeling of small, completely formalizable processes and their execution entirely on computers. This research direction has produced process modeling languages suitable for machine rather than human consumption. The MVP project, launched at the University of Maryland and continued at Universität Kaiserslautern, emphasizes the building of descriptive models of large, real-world processes and their uses by humans and computers for the purpose of understanding, analysis, execution guidance and improvement. The language MVP-L has been developed with these purposes in mind. In this paper, we motivate the need for MVP-L, introduce the language, and demonstrate its uses. It should be noticed, that MVP-L is only a prototype. We assume that further improvements to our language will be triggered by lessons learned from future applications and experiments.

Programmers spend a substantial fraction of their debugging time by navigating through source code, yet little is known about how programmers navigate. With the continuing growth in size and complexity of software, this fraction of time is likely to increase, which presents challenges to those seeking both to understand and address the needs of programmers during debugging. Therefore, we investigated the applicability a theory from another domain, namely information foraging theory, to the problem of programmers ’ navigation during software maintenance. The goal was to determine the theory’s ability to provide a foundational understanding that could inform future tool builders aiming to support programmer navigation. To perform this investigation, we first defined constructs and propositions for a new variant of information foraging theory for software maintenance. We then operationalized the constructs in different ways and built three executable models to allow for empirical investigation. We developed a simple information-scent-only model of navigation, a more advanced model of programmer navigation, named

It is well known that complexity affects software development and maintenance costs. In the Open Source context, the sharing of development and maintenance effort among developers is a fundamental tenet, which can be thought as a driver to reduce the impact of complexity on maintenance costs. However, complexity is a structural property of code, which is not quantitatively accounted for in traditional cost models. This paper introduces the concept of functional complexity, which weights the well-established McCabe's cyclomatic complexity metric to the number of interactive functional elements that an application provides to users. Such metric is used to analyze how Open Source development costs are affected by complexity. Traditional cost models, like CoCoMo, do not take into account the impact of complexity in estimating costs by means of accurate indicators. In contrast, results show how a higher complexity is associated with a lower design quality of code, and, hence, higher maintenance costs. Consequently, results suggest that a reliable effort estimation should be based on a precise evaluation of software complexity. Analyses are based on quality, complexity, and maintenance effort data collected for 59 Open Source applications (corresponding to 906 versions) selected from the SourceForge.net repository.