Quantitative Software Engineering Research Articles

Performance Evaluation of Some Selected Sorting Algorithms by the Use of Halstead Complexity Metrics

Complexity is developed to demonstrate feasible metrics for process obtaining objectives and quantifiable measurement, which may have numerous valuable applications in schedule and budget planning, cost estimation and optimal personnel task assignments a particular software. Also it aids the developer and practitioners in evaluating the software complexity due to its simplicity, which serves both as an analyzer and as a predicator in quantitative software engineering. In this research work, the Halstead complexity metrics were applied to three sorting algorithms and C-Sharp programming language was used to implement them. The results shows the variant performances of sorting algorithms and the best algorithm that can perform better in a defined system of application.

Conducting quantitative software engineering studies with Alitheia Core

Quantitative empirical software engineering research benefits mightily from processing large open source software repository data sets. The diversity of repository management tools and the long his...

Trustworthy systems through quantitative software engineering

Trustworthy systems through quantitative software engineering

Review of "Trustworthy Systems Through Quantitative Software Engineering by Lawrence Bernstein and C. M. Yuhas", IEEE Computer Society Press, 2005.

Review of "Trustworthy Systems Through Quantitative Software Engineering by Lawrence Bernstein and C. M. Yuhas", IEEE Computer Society Press, 2005.

Fuzzy Adaptive Logic Networks as Hybrid Models of Quantitative Software Engineering

Abstract Quantitative software engineering is aimed at designing models describing software processes and products. While being noticeably dominated by statistical regression models, this area also embraces advanced techniques of computational intelligence and knowledge-based engineering including rule-based models, fuzzy models, and neural networks. The rationale behind their usage in the setting of Software Engineering is threefold: (a) the underlying distributions of datasets may not adhere to general assumptions that are usually made in the setting of linear regression models: (b) it is beneficial to build interpretable models that in some sense are user-friendly; (c) the models should be advanced and computationally appealing so that they exhibit some nonlinear characteristics as well as are fully equipped with learning abilities. The proposed architecture comprises alogic-based skeleton (blueprint) and an array of generic perceptrons as being commonly encountered in neurocomputing. The hybrid nature...

The design of decision trees in the framework of granular data and their application to software quality models

In this study, we discuss the role of fuzzy sets regarded as a comprehensive algorithmic vehicle supporting the design of decision trees. Fuzzy sets help convert continuous attributes into discrete landmarks – fuzzy sets are afterwards exploited as the basic constructs in the optimization of a decision tree. The concept of fuzzy granulation realized via context-based clustering is aimed at the quantization (discretization) process. In contrast to so-called fuzzy decision trees, we enhance the development methodology of binary (Boolean) decision trees rather than generalizing them to the form of fuzzy constructs. Afterwards we solve the problem of quantifying complexity of software systems in the framework of decision trees. The advantages of this approach to quantitative software engineering are discussed in detail. Numerical examples are provided to illustrate the design methodology and provide a better insight into the algorithmic details as well as limitations of the decision trees approach.