Software Fault Prediction using Bio-Inspired Algorithms to Select the Features to be employed: An Empirical Study

In recent past, the use of bio-inspired algorithms got a significant attention in software fault predictions, where they can be used to select the most relevant features for a dataset aiming to increase the prediction accuracy of estimation techniques. The most-earlier and widely investigated algorithms are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). More recently, researchers have analyzed other algorithms inspired from nature. In this paper, we consider GA and PSO as baseline/benchmark algorithms and evaluate their performances against seven recently-employed bio-inspired algorithms and metaheuristics, namely Ant Colony Optimization, Bat Search, Bee Search, Cuckoo Search, Harmony Search, Multi-Objective Evolutionary Algorithm, and Tabu Search, for feature selection in software fault prediction. We present experiments with seven open source datasets and three estimation techniques: Random Forest, Support Vector Regression, and Linear Regression. We found that it is not always true that the recently introduced algorithms outperform the earlier introduced algorithms

Assessing the effectiveness of sequence diagrams in the comprehension of functional requirements: results from a family of five experiments

Modeling is a fundamental activity within the requirements engineering process and concerns the construction of abstract descriptions of requirements that are amenable to interpretation and validation. The choice of a modeling technique is critical whenever it is necessary to discuss the interpretation and validation of requirements. This is particularly true in the case of functional requirements and stakeholders with divergent goals and different backgrounds and experience. This paper presents the results of a family of experiments conducted with students and professionals to investigate whether the comprehension of functional requirements is influenced by the use of dynamic models that are represented by means of the UML sequence diagrams. The family contains five experiments performed in different locations and with 112 participants of different abilities and levels of experience with UML. The results show that sequence diagrams improve the comprehension of the modeled functional requirements in the case of high ability and more experienced participants.The authors wish to thank all the participants in the experiments. This research was partially supported by the MULTIPLE project (with ref. TIN2009-13838).Abrahao Gonzales, SM.; Gravino, .C.; Insfrán Pelozo, CE.; Scaniello, .G.; Tortora, .G. (2013). Assessing the effectiveness of sequence diagrams in the comprehension of functional requirements: results from a family of five experiments. IEEE Transactions on Software Engineering. 39(3):327-342. https://doi.org/10.1109/TSE.2012.27S32734239

An Ontology-Based Approach to Semi-Automate Systematic Literature Reviews

A Systematic Literature Review (SLR) allows us to combine and analyze data from multiple (published and unpublished) studies. Though it provides a complete and comprehensive empirical evidence of an area of interest, the results we usually get from the data synthesis phase of an SLR include huge tables and graphs and thus, for users, it is a tedious and time-consuming job to get the required results. In this work, we propose to semi-automate some steps which can be used to fetch the information from an SLR, beyond the traditional tables, graphs, and plots. The automation is performed using Semantic Web technologies like ontology, Jena API and SPARQL queries. The Semantic Web, also called Web 3.0, provides a common framework and thus allows us to share and re-use the data across the applications and enterprises. It can be used to integrate, extract, and infer the most relevant data required by the users, which are hidden behind the huge information on the Web. We also provide an easy-to-use user interface in order to allow users to perform different searches and find their required SLR results easily and quickly. Finally, we present the results of a preliminary user study performed to analyze the amount of time users need to extract their required information, both via the SLR tables and our proposal. The results revealed that with our system the users get their required information in less time compared to the manual system

How the use of design patterns affects the quality of software systems: A preliminary investigation

In this paper we analyze at the class level the quality of the software portions including classes participating in design patterns instances (DP classes) with respect to the remaining software portions (NoDP classes). The performed study is based on 10 software systems from which information about design pattern instances and CK (Chidamber and Kemerer) metrics were obtained by exploiting repositories of pattern instances and the tool Understand, respectively. The analysis revealed that the use of design patterns impacts on the quality of the software

Estimating web application development effort using COSMIC-FFP method

In the last few years, some researchers have proposed the use of COSMIC-FFP for effort prediction of Web applications. It is widely recognized, that a measure can be accepted only if its usefulness has been proved through some empirical studies. In this paper, we reported on an empirical study carried out using an industrial dataset and compared the results obtained with a previous analysis based on Web applications developed by academic students. We used an adaptation of COSMIC-FFP specifically conceived for Web applications as size measure and the Ordinary Least Square Regression as modelling technique. This analysis had a twofold goal: to verify whether COSMIC-FFP can provide good estimations and to analyse possible differences/similarities in the empirical results obtained with the two different datasets

Single and Multi Objective Genetic Programming for Software Development Effort Estimation

The idea of exploiting Genetic Programming (GP) to estimate software development effort is based on the observation that the effort estimation problem can be formulated as an optimization problem. Indeed, among the possible models, we have to identify the one providing the most accurate estimates. To this end a suitable measure to evaluate and compare different models is needed. However, in the context of effort estimation there does not exist a unique measure that allows us to compare different models but several different criteria (e.g., MMRE, Pred(25), MdMRE) have been proposed. Aiming at getting an insight on the effects of using different measures as fitness function, in this paper we analyzed the performance of GP using each of the five most used evaluation criteria. Moreover, we designed a Multi-Objective Genetic Programming (MOGP) based on Pareto optimality to simultaneously optimize the five evaluation measures and analyzed whether MOGP is able to build estimation models more accurate than those obtained using GP. The results of the empirical analysis, carried out using three publicly available datasets, showed that the choice of the fitness function significantly affects the estimation accuracy of the models built with GP and the use of some fitness functions allowed GP to get estimation accuracy comparable with the ones provided by MOGP

An Early Investigation on the Contribution of Class and Sequence Diagrams in Source Code Comprehension

We report the preliminary results of a controlled experiment conducted to analyze whether the combined use of UML class and sequence diagrams better supports source code comprehension with respect to the use of class and sequence diagrams alone. We also investigated which notation between class and sequence diagrams provides a better support in the execution of comprehension tasks on source code. The results suggest that it is better to use class and sequence diagrams together with respect to using either class or sequence diagrams alone. The difference in the source code comprehension is statistically significant with respect to the use of class diagrams alone, while is not statistically significant with respect to the sequence diagrams alone