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 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 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.

It has been claimed that dynamic prediction models can be used to help project managers make more accurate estimates than static prediction models. However, such a claim needs to be validated so that project managers can use dynamic models with confidence. In this paper we discuss an experiment we conducted in an academic environment that compared a dynamic model using Bayesian belief networks (BBN) with a static model involving the COCOMO and Akiyama models. The results from this experiment in fact validate the above claim. However we suggest replication of this experiment in order to increase confidence to our results.

Side-effects are widely believed to impede program comprehension and have a detrimental effect upon software maintenance. This paper introduces an algorithm for side-effect removal which splits the side-effects into their pure expression meaning and their state-changing meaning. Symbolic execution is used to determine the expression meaning, while transformation is used to place the state-changing part in a suitable location in a transformed version of the program. This creates a program which is semantically equivalent to the original but guaranteed to be free from side-effects. The paper also reports the results of an empirical study which demonstrates that the application of the algorithm causes a significant improvement in program comprehension.

Estimation of software size is a crucial activity among the tasks of software management. Work planning and subsequent estimations of the effort required are made based on the estimate of the size of the software product. Software size can be measured in several ways: lines of code (LOC) is a common measure and is usually one of the independent variables in equations for estimating several methods for estimating the final LOC count of a software system in the early stages. We report the results of the validation of the component-based method (initially proposed by Verner and Tate, 1988) for software sizing. This was done through the analysis of 46 projects involving more than 100,000 LOC of a fourth-generation language. We present several conclusions concerning the predictive capabilities of the method. We observed that the component-based method behaves reasonably, although not as well as expected for "global" methods such as Mark II function points for software size prediction. The main factor observed that affects the performance is the type of component.

A methodology is proposed for the development of computer-assisted tools for graphical mathematical modeling using a structured procedure. The goal is to define the steps for constructing software for graphical symbol manipulation in mathematical modeling in such a way that every step can be formally supervised. This method has already been applied to the definition of a technique, a mixed form of system dynamics and qualitative simulation, in a way that diminishes to a minimum the knowledge required for computer programming. Several areas of knowledge are involved: system modeling using various modeling techniques; a mathematical theory for testing the correctness of mathematical models; and software engineering and graph grammars which support the steps for constructing the software tool.>

A grammar that formally constructs the types of diagrams used in system dynamics is fully described. It belongs to the special kinds of grammars (attributed programmed graph grammars) that are applied to the construction of graphs and geometric figures. The flow diagrams used in system dynamics have been defined by 'attributed graphs' so that the approach could be applied. The grammar manipulates the graphs according to the requirements of the methodology. Basically, a method to achieve a formal program for interactive modeling upon any diagram, starting from the formal specification of the diagram, is presented.>