An Empirical Investigation into the Dimensions of Run-Time Coupling in Java Programs

Software quality is an important external software attribute that is di±cult to measure objectively. Several studies have identified a clear empirical relationship between static coupling metrics and software quality. However due to the nature of object-oriented programs, static metrics fail to quantify all the underlying dimensions of coupling, as program behaviour is a function of its operational environment as well as the complexity of the source code. In this paper a set of run-time object-oriented coupling metrics are described. A method of collecting such metrics which utilises the Java Platform Debug Architecture is described and a collection of Java programs from the SPECjvm98 benchmark suite are evaluated. A number of statistical techniques including descriptive statistics, a correlation study and principal component analysis are used to assess the fundamental properties of the measures and investigate whether they are redundant with respect to the Chidamber and Kemerer static CBO metric. Results to date indicate that run-time coupling metrics can provide an interesting and informative qualitative analysis of a program and complement existing static coupling metrics

A survey on software coupling relations and tools

Context Coupling relations reflect the dependencies between software entities and can be used to assess the quality of a program. For this reason, a vast amount of them has been developed, together with tools to compute their related metrics. However, this makes the coupling measures suitable for a given application challenging to find. Goals The first objective of this work is to provide a classification of the different kinds of coupling relations, together with the metrics to measure them. The second consists in presenting an overview of the tools proposed until now by the software engineering academic community to extract these metrics. Method This work constitutes a systematic literature review in software engineering. To retrieve the referenced publications, publicly available scientific research databases were used. These sources were queried using keywords inherent to software coupling. We included publications from the period 2002 to 2017 and highly cited earlier publications. A snowballing technique was used to retrieve further related material. Results Four groups of coupling relations were found: structural, dynamic, semantic and logical. A fifth set of coupling relations includes approaches too recent to be considered an independent group and measures developed for specific environments. The investigation also retrieved tools that extract the metrics belonging to each coupling group. Conclusion This study shows the directions followed by the research on software coupling: e.g., developing metrics for specific environments. Concerning the metric tools, three trends have emerged in recent years: use of visualization techniques, extensibility and scalability. Finally, some coupling metrics applications were presented (e.g., code smell detection), indicating possible future research directions. Public preprint [https://doi.org/10.5281/zenodo.2002001]

An Empirical Investigation into the Dimensions of Run-Time Coupling in Java Programs

Software quality is an important external software attribute that is di±cult to measure objectively. Several studies have identified a clear empirical relationship between static coupling metrics and software quality. However due to the nature of object-oriented programs, static metrics fail to quantify all the underlying dimensions of coupling, as program behaviour is a function of its operational environment as well as the complexity of the source code. In this paper a set of run-time object-oriented coupling metrics are described. A method of collecting such metrics which utilises the Java Platform Debug Architecture is described and a collection of Java programs from the SPECjvm98 benchmark suite are evaluated. A number of statistical techniques including descriptive statistics, a correlation study and principal component analysis are used to assess the fundamental properties of the measures and investigate whether they are redundant with respect to the Chidamber and Kemerer static CBO metric. Results to date indicate that run-time coupling metrics can provide an interesting and informative qualitative analysis of a program and complement existing static coupling metrics

An Empirical Investigation into the Dimensions of Run-Time Coupling in Java Programs

Software quality is an important external software attribute that is di±cult to measure objectively. Several studies have identified a clear empirical relationship between static coupling metrics and software quality. However due to the nature of object-oriented programs, static metrics fail to quantify all the underlying dimensions of coupling, as program behaviour is a function of its operational environment as well as the complexity of the source code. In this paper a set of run-time object-oriented coupling metrics are described. A method of collecting such metrics which utilises the Java Platform Debug Architecture is described and a collection of Java programs from the SPECjvm98 benchmark suite are evaluated. A number of statistical techniques including descriptive statistics, a correlation study and principal component analysis are used to assess the fundamental properties of the measures and investigate whether they are redundant with respect to the Chidamber and Kemerer static CBO metric. Results to date indicate that run-time coupling metrics can provide an interesting and informative qualitative analysis of a program and complement existing static coupling metrics

An Empirical Investigation into the Dimensions of Run-Time Coupling in Java Programs

Software quality is an important external software attribute that is di±cult to measure objectively. Several studies have identified a clear empirical relationship between static coupling metrics and software quality. However due to the nature of object-oriented programs, static metrics fail to quantify all the underlying dimensions of coupling, as program behaviour is a function of its operational environment as well as the complexity of the source code. In this paper a set of run-time object-oriented coupling metrics are described. A method of collecting such metrics which utilises the Java Platform Debug Architecture is described and a collection of Java programs from the SPECjvm98 benchmark suite are evaluated. A number of statistical techniques including descriptive statistics, a correlation study and principal component analysis are used to assess the fundamental properties of the measures and investigate whether they are redundant with respect to the Chidamber and Kemerer static CBO metric. Results to date indicate that run-time coupling metrics can provide an interesting and informative qualitative analysis of a program and complement existing static coupling metrics

A Study on Software Testability and the Quality of Testing in Object-Oriented Systems

Software testing is known to be important to the delivery of high-quality systems, but it is also challenging, expensive and time-consuming. This has motivated academic and industrial researchers to seek ways to improve the testability of software. Software testability is the ease with which a software artefact can be effectively tested. The first step towards building testable software components is to understand the factors – of software processes, products and people – that are related to and can influence software testability. In particular, the goal of this thesis is to provide researchers and practitioners with a comprehensive understanding of design and source code factors that can affect the testability of a class in object oriented systems. This thesis considers three different views on software testability that address three related aspects: 1) the distribution of unit tests in relation to the dynamic coupling and centrality of software production classes, 2) the relationship between dynamic (i.e., runtime) software properties and class testability, and 3) the relationship between code smells, test smells and the factors related to smells distribution. The thesis utilises a combination of source code analysis techniques (both static and dynamic), software metrics, software visualisation techniques and graph-based metrics (from complex networks theory) to address its goals and objectives. A systematic mapping study was first conducted to thoroughly investigate the body of research on dynamic software metrics and to identify issues associated with their selection, design and implementation. This mapping study identified, evaluated and classified 62 research works based on a pre-tested protocol and a set of classification criteria. Based on the findings of this study, a number of dynamic metrics were selected and used in the experiments that were then conducted. The thesis demonstrates that by using a combination of visualisation, dynamic analysis, static analysis and graph-based metrics it is feasible to identify central classes and to diagrammatically depict testing coverage information. Experimental results show that, even in projects with high test coverage, some classes appear to be left without any direct unit testing, even though they play a central role during a typical execution profile. It is contended that the proposed visualisation techniques could be particularly helpful when developers need to maintain and reengineer existing test suites. Another important finding of this thesis is that frequently executed and tightly coupled classes are correlated with the testability of the class – such classes require larger unit tests and more test cases. This information could inform estimates of the effort required to test classes when developing new unit tests or when maintaining and refactoring existing tests. An additional key finding of this thesis is that test and code smells, in general, can have a negative impact on class testability. Increasing levels of size and complexity in code are associated with the increased presence of test smells. In addition, production classes that contain smells generally require larger unit tests, and are also likely to be associated with test smells in their associated unit tests. There are some particular smells that are more significantly associated with class testability than other smells. Furthermore, some particular code smells can be seen as a sign for the presence of test smells, as some test and code smells are found to co-occur in the test and production code. These results suggest that code smells, and specifically certain types of smells, as well as measures of size and complexity, can be used to provide a more comprehensive indication of smells likely to emerge in test code produced subsequently (or vice versa in a test-first context). Such findings should contribute positively to the work of testers and maintainers when writing unit tests and when refactoring and maintaining existing tests