Computer Languages and Systems

This article ex-plores some require-ments for applying qualitative simula-tion algorithms to system dynamics models. The field of qualitative simula-tion lacks a specific procedure for modeling systems (subjects of its mo-dels are generally physical systems). Bringing together ...

En el presente artículo se realiza una propuesta para la medición práctica de la coordinación dentro del proceso software utilizando como guías las prácticas clave de los modelos de madurez CMM, el proceso de medición Goal-Questions-Indicators-Measures, más conocido como GQ(I)M, así como una aplicación software que implementa el proceso de medición PSM.

Software engineering, as a discipline, is not yet as mature as other engineering disciplines and it lacks criteria to assess, from an engineering perspective, the current content of its body of knowledge as embedded in the SWEBOK Guide. What is then the engineering knowledge that should be embedded within software engineering? Vincenti, in his book 'What engineers know and how they know it' has proposed a taxonomy of engineering knowledge types. To investigate software engineering from an engineering perspective, these Vincenti's categories of engineering knowledge are used to identify relevant engineering criteria and their presence in SWEBOK.

Abstract. The goal of this empirical study is to compare the semantic comprehension of three different notations for representing the dynamic behaviour in unified modelling language (UML): (a) sequence diagrams, (b) collaboration diagrams, and (c) state diagrams. ...

This paper reports the results of a study on the impact of a type of side effect (SE) upon program comprehension. We applied a crossover design on different tests involving fragments of C code that include increment and decrement operators. Each test had an SE version and a side-effect-free (SEF) counterpart. The variables measured in the treatments were the number of correct answers and the time spent in answering. The results show that the side-effect operators considered significantly reduce performance in comprehension-related tasks, providing empirical justification for the belief that side effects are harmful.

... Available online 15 November 2000. Abstract. The question of finding a function for softwarecost estimation is a long-standing issue in the software engineering field. ... 3. Genetic programming for software cost estimation. GP is a type of evolutionary computation technique. ...

El objetivo de este estudio exploratorio es obtener un subconjunto de métricas de diagramas de UML, que resuman la información que proporcionan un conjunto inicial de 21 métricas, las cuales miden diferentes aspectos de atributos de los diagramas como tamaño, herencia, etc. La técnica fundamental utilizada en el estudio fue el Análisis de Componentes Principales (ACP). El estudio se ha realizado sobre 78 diagramas de clases reales, procedentes del producto desarrollado en una empresa, y las mediciones de las 21 métricas se obtuvieron mediante el módulo "Quality Assurance" de la herramienta CASE Together. La principal conclusión del estudio es que las 20 dimensiones o métricas, consideradas, se pueden reducir a cuatro factores o dimensiones, de los cuales el factor1 contiene información, fundamentalmente, de la herencia y complejidad de la herencia, el factor2 recoge la información relativa al acoplamiento, el factor3 recoge la cohesión, y el factor4 recoge la visibilidad de las clases. Además la métrica PPKGM no esta correlacionada con las otras métricas, por lo que se excluyó del estudio de ACP. Palabras clave: diagramas de UML, métricas, análisis exploratorio de datos, análisis de componentes principales.

Imbalanced data is a common problem in data mining when dealing with classification problems, where samples of a class vastly outnumber other classes. In this situation, many data mining algorithms generate poor models as they try to optimize the overall accuracy and perform badly in classes with very few samples. Software Engineering data in general and defect prediction datasets are not an exception and in this paper, we compare different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently. We have used the well-known NASA datasets curated by Shepperd et al. There are differences in the results depending on the characteristics of the dataset and the evaluation metrics, especially if duplicates and inconsistencies are removed as a preprocessing step.

This article proposes a new measure to compare soft computing methods for software estimation. This new measure is based on the concepts of Equivalence Hypothesis Testing (EHT). Using the ideas of EHT, a dimensionless measure is defined using the Minimum Interval of Equivalence and a random estimation. The dimensionless nature of the metric allows us to compare methods independently of the data samples used. The motivation of the current proposal comes from the biases that other criteria show when applied to the comparison of software estimation methods. In this work, the level of error for comparing the equivalence of methods is set using EHT. Several soft computing methods are compared, including genetic programming, neural networks, regression and model trees, linear regression (ordinary and least mean squares) and instance-based methods. The experimental work has been performed on several publicly available datasets. Given a dataset and an estimation method we compute the upper point of Minimum Interval of Equivalence, MIEu, on the confidence intervals of the errors. Afterwards, the new measure, MIEratio, is calculated as the relative distance of the MIEu to the random estimation. Finally, the data distributions of the MIEratios are analysed by means of probability intervals, showing the viability of this approach. In this experimental work, it can be observed that there is an advantage for the genetic programming and linear regression methods by comparing the values of the intervals.

Bayesian Networks are becoming increasingly popular within the Software Engineering research community as an effective method of analysing collected data. This paper deals with the creation and the use of Bayesian networks and Bayesian classifiers in project management. We illustrate this process with an example in the context of software estimation that uses the Maxwell's dataset [17] (it is a subset of the Finnish dataset-STTF-). We highlight some of the difficulties and challenges of using Bayesian networks and Bayesian classifiers. We discuss how the Bayesian approach can be used as a viable technique in Software Engineering in general and for project management in particular; and also the challenges and the open issues.

This paper presents SoftMagnet, a new multivariate analysis model for controlling and managing the processes of software project development. SoftMagnet uses metaphors and visual representation techniques to explore several key indicators in order to support problem detection and resolution. The resulting visualization addresses diverse management tasks, such as tracking of deviations from the plan, analysis of patterns of failure detection and correction, overall assessment of change management policies, and ...