Complexity Of Object-oriented Software Research Articles

Efficacy of Inheritance Aspect in Software Fault Prediction—A Survey Paper

Software fault prediction (SFP) is a research area that helps development and testing process deliver software of good quality. Software metrics are of various types and are used in SFP for measurements. Inheritance is a prominent feature, which measures the depth, breadth, and complexity of object-oriented software. A few studies exclusively addressed the efficacy of inheritance in SFP. This provokes the need to identify the potential ingredients associated with inheritance, which can be helpful in SFP. In this paper, our aim is to collecting, organizing, categorizing, and investigating published fault prediction studies. Findings include identification of 54 inheritance metrics, 78 public datasets with various combinations of 10 inheritance metrics, 60% use of method level & use of private datasets, an increased number of studies using machine learning approaches. This study will facilitate scholars to studying previous literature on software fault prediction having software metrics, with their methods, public data sets, performance evaluation of machine learning algorithms, and findings of experimental results in a comfortable, and efficient way, emphasizing the inherited aspect specifically.

Analyzing maintainability and reliability of object-oriented software using weighted complex network

Modeling software systems using complex networks can be an effective technique for analyzing the complexity of software systems. To enhance the technique, the structure of a complex network can be extended by assigning a weight to the edges of the complex network to denote the strength of communicational cohesion between a pair of related software components. This paper proposes an approach to represent an object-oriented software system using a weighted complex network in order to capture its structural characteristics, with respect to its maintainability and reliability. Nodes and edges are modeled based on the complexities of classes and their dependencies. Graph theory metrics are applied onto the transformed network with the purpose to evaluate the software system. Comparative analysis is performed using 40 object-oriented software systems, with different levels of maintenance effort. We found that common statistical patterns from the software systems can be identified easily. It is when these software systems are grouped and compared based on their levels of maintenance effort that their statistical patterns are more distinguishable to represent some common behavior and structural complexity of object-oriented software. The evaluations indicate that the proposed approach is capable of identifying software components that violate common software design principles.

On the Application of Information Entropy-based Multi-attribute Decision in UML Class Diagram Metrics

The research, development and applications of software measurement have been carried out for more than forty years. Many researchers have done much in it, obtained lots of theoretical results, and developed a series of practical applications. At present, with the rapid development of object-oriented technology used in software theories and application, how to measure software in an effectively and scientifically way is becoming a hot and difficult issue. Class diagram as the most important UML model, its reasonable design has a remarkable effect on the ultimate system. This paper applies information entropy-based multi-attribute decision in UML class diagram metrics, providing a method to measure UML class diagram complexity weights. Its attributes out of information entropy and attributes’ weigh vectors can measure the complexity of objectoriented software effectively and scientifically. Besides, case analysis can prove this metric’s usability. The more precise experiment outcome has proved that the method is connected to human’s experience, and also can be applied to improve software quality.

Structural Epochs in Software

A way to measure the complexity of object-oriented software involves topological features of the code's hierarchical organization at the method, class, package, and component levels. Sangwan et al (2008) suggested that as certain software products evolve, this complexity shifts from lower to higher structural levels, or vice-versa. They studied three widely used open source software programs and showed that these structural shifts called “epochs” were present and suspected that this phenomenon was pervasive. To support or refute this assertion, 30 open source programs were studied and structural shifts in complexity were found significantly in 27 of them. In those projects where no complexity shift was evident, no refactoring had occurred. These findings further suggest that in large, open source software projects, when refactoring occurs a shifting in complexity from one level to another will occur.

Measurement of object-oriented software spatial complexity

One of the important activities of the maintenance phase is to understand the source-code first, and then change it. Understandability of the software gets affected by psychological complexity of the source-code and cognitive abilities are needed to understand it. The correlation between the orientation and location of various entities with their processing needs to be established by the programmers, which requires spatial abilities. These spatial abilities play an important role in object-oriented software, in which the use of data as well as the methods of the class needs to be understood in a combined way. This paper presents two measures of spatial complexity of object-oriented software, which are based on definition and usage of classes and objects. The values of proposed measures get affected suitably because of inheritance and polymorphism as well, due to change in the distances. The significance of object-oriented spatial complexity has been demonstrated with the help of 15 object-oriented projects of varied length and results have been validated with the help of reverse engineering data and perfective maintenance data.

Using Z to specify object-oriented software complexity measures

Software metrics have been a key strategy for software engineers to assure software quality and measure software complexity for many years. However, the complexity metrics of object inheritance hierarchies have not yet been carefully studied. This paper proposes an inheritance level-based metric for measuring the object-oriented software complexity of an inheritance hierarchy. We introduce an algorithm which is proved to be complete and sound to decompose an inheritance hierarchy into a number of measured elements, namely, unit repeated inheritances (URIs). We use the Z specification to model the inheritance hierarchy and describe an inheritance level technique (ILT) based on URIs as a guide to measure the inheritance hierarchy. The proposed metric shows that inheritance has a close relation to object-oriented software complexity. The metric also reveals that overuse of repeated (multiple) inheritance will increase software complexity and thus is prone to implicit software errors. The measurement of inheritance metrics is formed based on the proposed mechanism.

Empirical evaluation of reuse sensitiveness of complexity metrics

Measuring software products and processes is essential for improving software productivity and quality. In order to evaluate the complexity of object-oriented software, several complexity metrics have been proposed. Among them, Chidamber and Kemerer’s metrics are the most well-known for object-oriented software. Their metrics evaluate the complexity of the classes in terms of internal, inheritance, and coupling complexity. Though the reused classes of the class library usually have better quality than the newly-developed ones, their metrics deal with inheritance and coupling complexity in the same way. This article first proposes a revision of the Chidamber and Kemerer’s metrics which can be applied to software which had been constructed by reusing software components. Then, we give an analysis of data collected from the development of an object-oriented software using a GUI framework. We compare the original metrics with the revised ones by evaluating the accuracy of estimating the effort to fix faults and show the validity and usefulness of the revised metrics.

Using Z to specify object-oriented software complexity measures

Software metrics have been a key strategy for software engineers to assure software quality and measure software complexity for many years. However, the complexity metrics of object inheritance hierarchies have not yet been carefully studied. This paper proposes an inheritance level-based metric for measuring the object-oriented software complexity of an inheritance hierarchy. We introduce an algorithm which is proved to be complete and sound to decompose an inheritance hierarchy into a number of measured elements, namely, unit repeated inheritances (URIs). We use the Z specification to model the inheritance hierarchy and describe an inheritance level technique (ILT) based on URIs as a guide to measure the inheritance hierarchy. The proposed metric shows that inheritance has a close relation to object-oriented software complexity. The metric also reveals that overuse of repeated (multiple) inheritance will increase software complexity and thus is prone to implicit software errors. The measurement of inheritance metrics is formed based on the proposed mechanism.

Integration Object-Oriented Software Testing and Metrics

Software testing methodologies and metrics are key issues to improve and assure software quality. They are important areas in the research of Software Engineering. However, not many focus on the testing criteria and metrics evaluation of a class hierarchy. This paper introduces an important concept named URI (Unit Repeated Inheritance) to realize integrated object-oriented testing and object-oriented metrics. The approach describes an ILT (Inheritance Level Technique) method as a guide to test object-oriented software errors and measure the software complexity of a class hierarchy. The proposed technique shows that inheritance has a close relation to object-oriented software complexity and reveals that overuse of repeated(multiple) inheritance will increase software complexity and be prone to implicit software errors. Also, two test criteria: intra level first and inter level first are presented.