An empirical investigation of an object-oriented software system

This is the post print version of the article. The official published version can be obtained from the link below.This paper describes an empirical investigation into an industrial object-oriented (OO) system comprised of 133,000 lines of C++. The system was a subsystem of a telecommunications product and was developed using the Shlaer-Mellor method. From this study, we found that there was little use of OO constructs such as inheritance and, therefore, polymorphism. It was also found that there was a significant difference in the defect densities between those classes that participated in inheritance structures and those that did not, with the former being approximately three times more defect-prone. We were able to construct useful prediction systems for size and number of defects based upon simple counts such as the number of states and events per class. Although these prediction systems are only likely to have local significance, there is a more general principle that software developers can consider building their own local prediction systems. Moreover, we believe this is possible, even in the absence of the suites of metrics that have been advocated by researchers into OO technology. As a consequence, measurement technology may be accessible to a wider group of potential users

Does OO sync with the way we think?

Given that corrective-maintenance costs already dominate the software life cycle and look set to increase significantly, reliability in the form of reducing such costs should be the most important software improvement goal. Yet the results are not promising when we review recent corrective-maintenance data for big systems in general and for OO in particular-possibly because of mismatches between the OO paradigm and how we think

How Scale Affects Structure in Java Programs

Many internal software metrics and external quality attributes of Java programs correlate strongly with program size. This knowledge has been used pervasively in quantitative studies of software through practices such as normalization on size metrics. This paper reports size-related super- and sublinear effects that have not been known before. Findings obtained on a very large collection of Java programs -- 30,911 projects hosted at Google Code as of Summer 2011 -- unveils how certain characteristics of programs vary disproportionately with program size, sometimes even non-monotonically. Many of the specific parameters of nonlinear relations are reported. This result gives further insights for the differences of "programming in the small" vs. "programming in the large." The reported findings carry important consequences for OO software metrics, and software research in general: metrics that have been known to correlate with size can now be properly normalized so that all the information that is left in them is size-independent.Comment: ACM Conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA), October 2015. (Preprint

Impact of Mediatedrelations As Confounding Factor on Cohesion and Coupling Metrics: For Measuring Fault Proneness in OO Software Quality Assessment

Mediated class relations and method calls as a confounding factor on coupling and cohesion metrics to assess the fault proneness of object oriented software is evaluated and proposed new cohesion and coupling metrics labeled as mediated cohesion (MCH) and mediated coupling (MCO) proposed. These measures differ from the majority of established metrics in two respects: they reflect the degree to which entities are coupled or resemble each other, and they take account of mediated relations in couplings or similarities. An empirical comparison of the new measures with eight established metrics is described. The new measures are shown to be consistently superior at measure the fault proneness

An Empirical Study on the Procedure to Derive Software Quality Estimation Models

Software quality assurance has been a heated topic for several decades. If factors that influence software quality can be identified, they may provide more insight for better software development management. More precise quality assurance can be achieved by employing resources according to accurate quality estimation at the early stages of a project. In this paper, a general procedure is proposed to derive software quality estimation models and various techniques are presented to accomplish the tasks in respective steps. Several statistical techniques together with machine learning method are utilized to verify the effectiveness of software metrics. Moreover, a neuro-fuzzy approach is adopted to improve the accuracy of the estimation model. This procedure is carried out based on data from the ISBSG repository to present its empirical value

Evaluación de mantenibilidad de un gestor de contenidos open source utilizando métricas de orientación a objetos

Los aspectos vinculados a la mantenibilidad contribuyen a la calidad del software y también a la decisión favorable de adoptarlo para su uso, en el caso particular del software libre. En este artículo se presentan los resultados de la evaluación de un gestor de contenidos de software libre con el objetivo de conocer el grado de mantenibilidad del mismo, aplicando métricas Orientadas a Objetos, utilizando la metodología Goal-Question-Metrics. De acuerdo a los resultados obtenidos se puede presumir que el software Joomla es un proyecto estable y maduro, en el que se observa un crecimiento sistemático a lo largo del tiempo, y mejoras en el aspecto específico de mantenibilidad evaluado.Sociedad Argentina de Informática e Investigación Operativ