Comprehension of object-oriented software cohesion: The empirical quagmire

Chidamber and Kemerer (1991) proposed an object-oriented (OO) metric suite which included the Lack of Cohesion Of Methods (LCOM) metric. Despite considerable effort both theoretically and empirically since then, the software engineering community is still no nearer finding a generally accepted definition or measure of OO cohesion. Yet, achieving highly cohesive software is a cornerstone of software comprehension and hence, maintainability. In this paper, we suggest a number of suppositions as to why a definition has eluded (and we feel will continue to elude) us. We support these suppositions with empirical evidence from three large C++ systems and a cohesion metric based on the parameters of the class methods; we also draw from other related work. Two major conclusions emerge from the study. Firstly, any sensible cohesion metric does at least provide insight into the features of the systems being analysed. Secondly however, and less reassuringly, the deeper the investigative search for a definitive measure of cohesion, the more problematic its understanding becomes; this casts serious doubt on the use of cohesion as a meaningful feature of object-orientation and its viability as a tool for software comprehension

Regression Testing of Object-Oriented Software based on Program Slicing

As software undergoes evolution through a series of changes, it is necessary to validate these changes through regression testing. Regression testing becomes convenient if we can identify the program parts that are likely to be affected by the changes made to the programs as part of maintenance activity. We propose a change impact analysis mechanism as an application of slicing. A new slicing method is proposed to decompose a Java program into affected packages, classes, methods and statements identified with respect to the modification made in the program. The decomposition is based on the hierarchical characteristic of Java programs. We have proposed a suitable intermediate representation for Java programs that shows all the possible dependences among the program parts. This intermediate representation is used to perform the necessary change impact analysis using our proposed slicing technique and identify the program parts that are possibly affected by the change made to the program. The packages, classes, methods, and statements thus affected are identified by traversing the intermediate graph, first in the forward direction and then in the backward direction. Based on the change impact analysis results, we propose a regression test selection approach to select a subset of the existing test suite. The proposed approach maps the decomposed slice (comprising of the affected program parts) with the coverage information of the existing test suite to select the appropriate test cases for regression testing. All the selected test cases in the new test suite are better suited for regression testing of the modified program as they execute the affected program parts and thus have a high probability of revealing the associated faults. The regression test case selection approach promises to reduce the size of regression test suite. However, sometimes the selected test suite can still appear enormous, and strict timing constraints can hinder execution of all the test cases in the reduced test suite. Hence, it is essential to minimize the test suite. In a scenario of constrained time and budget, it is difficult for the testers to know how many minimum test cases to choose and still ensure acceptable software quality. So, we introduce novel approaches to minimize the test suite as an integer linear programming problem with optimal results. Existing research on software metrics have proven cohesion metrics as good indicator of fault-proneness. But, none of these proposed metrics are based on change impact analysis. We propose a changebased cohesion measure to compute the cohesiveness of the affected program parts. These cohesion values form the minimization criteria for minimizing the test suite. We formulate an integer linear programming model based on the cohesion values to optimize the test suite and get optimal results. Software testers always face the dilemma of enhancing the possibility of fault detection. Regression test case prioritization promises to detect the faults early in the retesting process. Thus, finding an optimal order of execution of the selected regression test cases will maximize the error detection rates at less time and cost. We propose a novel approach to identify a prioritized order of test cases in a given regression selected test suite that has a high chance of fault exposing capability. It is very likely that some test cases execute some program parts that are more prone to errors and have a greater possibility of detecting more errors early during the testing process. We identify the fault-proneness of the affected program parts by finding their coupling values. We propose to compute a new coupling metric for the affected program parts, named affected change coupling, based on which the test cases are prioritized. Our analysis shows that the test cases executing the affected program parts with high affected change coupling have a higher potential of revealing faults early than other test cases in the test suite. Testing becomes convenient if we identify the changes that require rigorous retesting instead of laying equal focus to retest all the changes. Thus, next we propose an approach to save the effort and cost of retesting by identifying and quantifying the impact of crosscutting changes on other parts of the program. We propose some metrics in this regard that are useful to the testers to take early decision on what to test more and what to test less

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Sustainability evaluation of software architectures

Long-living software systems are sustainable if they can be cost-efficiently maintained and evolved over their entire life-cycle. The quality of software architectures determines sus-tainability to a large extent. Scenario-based software archi-tecture evaluation methods can support sustainability anal-ysis, but they are still reluctantly used in practice. They are also not integrated with architecture-level metrics when evaluating implemented systems, which limits their capabil-ities. Existing literature reviews for architecture evaluation focus on scenario-based methods, but do not provide a criti-cal reflection of the applicability of such methods for sustain-ability evaluation. Our goal is to measure the sustainabil-ity of a software architecture both during early design us-ing scenarios and during evolution using scenarios and met-rics, which is highly relevant in practice. We thus provide a systematic literature review assessing scenario-based meth-ods for sustainability support and categorize more than 40 architecture-level metrics according to several design prin-ciples. Our review identifies a need for further empirical research, for the integration of existing methods, and for the more efficient use of formal architectural models. 1

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]

Dependence Communities in Source Code

Dependence between components in natural systems is a well studied phenomenon in the form of biological and social networks. The concept of community structure arises from the analysis of social networks and has successfully been applied to complex networks in other fields such as biology, physics and computing. We provide empirical evidence that dependence between statements in source code gives rise to community structure. This leads to the introduction of the concept of dependence communities in software and we provide evidence that they reflect the semantic concerns of a program. Current definitions of sliced-based cohesion and coupling metrics are not defined for procedures which do not have clearly defined output variables and definitions of output variable vary from study-to-study. We solve these problems by introducing corresponding new, more efficient forms of slice-based metrics in terms of maximal slices. We show that there is a strong correlation between these new metrics and the old metrics computed using output variables. We conduct an investigation into dependence clusters which are closely related to dependence communities. We undertake an empirical study using definitions of dependence clusters from previous studies and show that, while programs do contain large dependence clusters, over 75% of these are not ‘true’ dependence clusters. We bring together the main elements of the thesis in a study of software quality, investigating their interrelated nature. We show that procedures that are members of multiple communities have a low cohesion, programs with higher coupling have larger dependence communities, programs with large dependence clusters also have large dependence communities and programs with high modularity have low coupling. Dependence communities and maximal-slice-based metrics have a huge number of potential applications including program comprehension, maintenance, debugging, refactoring, testing and software protection

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

A Planning Approach to Migrating Domain-specific Legacy Systems into Service Oriented Architecture

The planning work prior to implementing an SOA migration project is very important for its success. Up to now, most of this kind of work has been manual work. An SOA migration planning approach based on intelligent information processing methods is addressed to semi-automate the manual work. This thesis will investigate the principle research question: “How can we obtain SOA migration planning schemas (semi-) automatically instead of by traditional manual work in order to determine if legacy software systems should be migrated to SOA computation environment?”. The controlled experiment research method has been adopted for directing research throughout the whole thesis. Data mining methods are used to analyse SOA migration source and migration targets. The mined information will be the supplementation of traditional analysis results. Text similarity measurement methods are used to measure the matching relationship between migration sources and migration targets. It implements the quantitative analysis of matching relationships instead of common qualitative analysis. Concretely, an association rule and sequence pattern mining algorithms are proposed to analyse legacy assets and domain logics for establishing a Service model and a Component model. These two algorithms can mine all motifs with any min-support number without assuming any ordering. It is better than the existing algorithms for establishing Service models and Component models in SOA migration situations. Two matching strategies based on keyword level and superficial semantic levels are described, which can calculate the degree of similarity between legacy components and domain services effectively. Two decision-making methods based on similarity matrix and hybrid information are investigated, which are for creating SOA migration planning schemas. Finally a simple evaluation method is depicted. Two case studies on migrating e-learning legacy systems to SOA have been explored. The results show the proposed approach is encouraging and applicable. Therefore, the SOA migration planning schemas can be created semi-automatically instead of by traditional manual work by using data mining and text similarity measurement methods