Predict fault-prone classes using the complexity of UML class diagram

Abstract

Complexity is an important attribute to determine the software quality. Software complexity can be measured during the design phase before implementation of system. At the design phase, UML class diagram is the important diagram to show the relationships among the classes of objects in the system. In this paper, we measure the complexity of object-oriented software at design phase to predict the fault-prone classes. The ability to predict the fault-prone classes can provide guidance for software testing and improve the effectiveness of development process. We constructed the Naive Bayesian and k-Nearest Neighbors model to find the relationship between the design complexity and fault-proneness. The proposed models are empirically evaluated using four version of JEdit. The models had been validated using 10-fold cross validation. The performance of prediction models were evaluated by goodnessof-fit criteria and Receiver Operating Characteristic (ROC) analysis. Results obtained from our case study showed the average of models developed by design complexity can predict up to 70% fault-prone classes in object oriented software. It is a better an early indicator of software quality.

Faltas en el aprendizaje del modelado de clases y casos de uso: una revisión sistemática

“…Por otro lado, Halim (2013) propone un modelo para medir la complejidad del software orientado a objetos en la fase de diseño para predecir las clases propensas a fallas. Así mimo, el autor menciona que el modelo de predicción se construye 12 Uso incorrecto de generalizaciones/ especializaciones.…”

“…En cuanto a las herramientas para la detección de faltas en los diagramas de clases, Halim (2013) comenta que para la recopilación de datos utilizó cuatro versiones de JEdit, esta herramienta es un proyecto de código abierto basado en JAVA; sin embargo, tal como se comentó en la respuesta a la pregunta de investigación anterior, no se mencionan las faltas que se detectan en los diagramas de clases. Vanwormhoudt et al (2017) presentan el uso plantillas aspectuales, el cual requiere que los parámetros formen un modelo de sistemas que inyecten nuevas funcionalidades.…”

Optimized Test Case Generation for Object Oriented Systems Using Weka Open Source Software

“…(Gyimothy etal.,2008)presentedanempiricalvalidationofOOmetricsforfaultpredictiononopensource software.AutomaticmethodforcalculationofOOmetricsforC++hasbeenpresentedandauthors suggestedthatcbometricisthebestmetricforpredictingfault-pronenessofC++classes. (Halim, 2013) showed revealing of 70% of faults prone classes for OO software. (Hosseini et al, 2017) advocatedcrossprojectdefectprediction(CPDP)studies.…”

An Automated Approach for Classifying Reverse-Engineered and Forward-Engineered UML Class Diagrams

“…Halim [146] proposed a method to Predict fault-prone classes using the complexity metrics of UML class diagram. The prediction models were built Bagheri and Gasevic conclude that NLeaf, NVC, CC and FoC are the most suitable features for predicting the aforementioned sub-characteristics of maintainability.…”