Cohesion Metrics Research Articles

Measuring Class Cohesion Based on Client Similarities Between Method Pairs: An Improved Approach That Supports Refactoring

Class cohesion is an important quality attribute that has an impact on other quality attributes such understandability, testability, and maintainability. Therefore, classes with low cohesion should be refactored in order to improve their overall qualities. Many cohesion metrics have been introduced in the literature to automatically assess the quality of the class and support refactoring activities. Most existing metrics measure the class cohesion based on how the methods of a class are internally related to each other, while a few metrics measure the class cohesion based on how the methods are externally used by the clients of the class. Unfortunately, the existing client-based cohesion metrics cannot automatically support refactoring techniques such as the Extract Class refactoring. Therefore, this article proposes a new client-based cohesion metric that can be used to automatically identify Extract Class refactoring opportunities. The proposed metric is theoretically evaluated by proving the compliance of the metric to the mathematical cohesion properties, while it is empirically evaluated by conducting a large case study on three systems to compare the metric with other cohesion metrics. Finally, the paper introduces and evaluates an Extract Class refactoring approach based the proposed cohesion metric.

Domain Ontology Construction and Evaluation for the Entire Process of Software Testing

As an important part of software engineering, software testing is a knowledge-intensive work. In the process of software testing, inconsistent knowledge expression, diverse knowledge carriers, and a few experienced people have mastered most of the knowledge, which hinders the transfer and sharing of domain knowledge. Ontology is widely used in various stages of software engineering to define the semantic relationship between relevant information and knowledge. To solve the problem of knowledge silo in the process of software testing, this article forms an Entire Process Ontology on Software Testing (EPOST). EPOST covers the concepts and relationships of software testing process information, software test object information, and software defect information. The concepts and terms in the ontology are extracted from ISTQB, SWEBOK, IEEE std.829-2008 standard, and IEEE std.610.12-1990 standard. We adopt a comprehensive ontology construction method based on Dev. 101 method and Methontology method. The developed ontology is successfully evaluated by using validation and verification tests. Ontology verification uses an improved FOCA evaluation method by adding a cohesion metric. The evaluation result infers that EPOST has a high quality of ontology and good domain coverage, and achieves the purpose of ontology construction. Finally, we make a case study on the role of EPOST in software testing process. The results show that ontology-based application in the software testing process can promote the sharing and transmission of domain knowledge, and improve the testing process and testing quality.

Social Cohesion in Australia: Comparing Church and Community

In a context of increasing ethnic and religious diversity, Australia’s future prosperity may depend, in part, on the ability to maintain social cohesion. Drawing on the framework developed by the Scanlon Foundation Social Cohesion Research Program, this study examines data from the 2016 National Church Life Survey and the 2016 Australian Community Survey to compare levels of social cohesion among Australian churchgoers and among the general population. Social cohesion metrics were stronger among churchgoers than the wider population across the domains of belonging, social justice, civic participation, acceptance of others and worth. Differences were also observed between Christian denominations on most domains, but with few exceptions, social cohesion among churchgoers from each denomination was still higher than that observed for all Australians. The findings suggest that Christian groups play a positive role in the promotion of social cohesion by building both bridging and bonding social capital among those who participate, but that these groups are unlikely to be a significant source of agitation to prevent some of the greatest contemporary threats to social cohesion.

Design and Implementation of Component based Metric for Software Complexity Measurement

This paper designs a single component based metric to measure the complexity of any software in any phase of software development life cycle. The metric is designed on the basis of existing coupling and cohesion metrics like normalized hamming code (NHD), lack of cohesion in methods (LCOM), conceptual coupling(CoCC), structural and semantic coupling metric(SSCM). The designed metric also covers the coupling between parent and its inherited class, static import, anonymous class contribution and the coupling between inner and outer class to analyze the complexity of software precisely. The analysis of the metric has been done on seven industrial and academic projects against existing state of art coupling and cohesion metric i.e. NHD, COCC, SSCM, LCOM5 and method attribute cohesion metric. The result and analysis shows the significance of the designed metric.

On the impact of service-oriented patterns on software evolvability: a controlled experiment and metric-based analysis.

BackgroundDesign patterns are supposed to improve various quality attributes of software systems. However, there is controversial quantitative evidence of this impact. Especially for younger paradigms such as service- and Microservice-based systems, there is a lack of empirical studies.ObjectiveIn this study, we focused on the effect of four service-based patterns—namely Process ion, Service Façade, Decomposed Capability, and Event-Driven Messaging—on the evolvability of a system from the viewpoint of inexperienced developers.MethodWe conducted a controlled experiment with Bachelor students (N = 69). Two functionally equivalent versions of a service-based web shop—one with patterns (treatment group), one without (control group)—had to be changed and extended in three tasks. We measured evolvability by the effectiveness and efficiency of the participants in these tasks. Additionally, we compared both system versions with nine structural maintainability metrics for size, granularity, complexity, cohesion, and coupling.ResultsBoth experiment groups were able to complete a similar number of tasks within the allowed 90 min. Median effectiveness was 1/3. Mean efficiency was 12% higher in the treatment group, but this difference was not statistically significant. Only for the third task, we found statistical support for accepting the alternative hypothesis that the pattern version led to higher efficiency. In the metric analysis, the pattern version had worse measurements for size and granularity while simultaneously having slightly better values for coupling metrics. Complexity and cohesion were not impacted.InterpretationFor the experiment, our analysis suggests that the difference in efficiency is stronger with more experienced participants and increased from task to task. With respect to the metrics, the patterns introduce additional volume in the system, but also seem to decrease coupling in some areas.ConclusionsOverall, there was no clear evidence for a decisive positive effect of using service-based patterns, neither for the student experiment nor for the metric analysis. This effect might only be visible in an experiment setting with higher initial effort to understand the system or with more experienced developers.

A dataflow-driven approach to identifying microservices from monolithic applications

Microservices architecture emphasizes employing multiple small-scale and independently deployable microservices, rather than encapsulating all function capabilities into one monolith. Correspondingly, microservice-oriented decomposition, which has been identified to be an extremely challenging task, plays a crucial and prerequisite role in developing microservice-based systems. To address the challenges in such a task, we propose a dataflow-driven semi-automatic decomposition approach. In particular, a four-step decomposition procedure is defined: (1) conduct the business requirement analysis to generate use case and business logic specification; (2) construct the fine-grained Data Flow Diagrams (DFD) and the process-datastore version of DFD (DFDPS) representing the business logics; (3) extract the dependencies between processes and datastores into decomposable sentence sets; and (4) identify candidate microservices by clustering processes and their closely related datastores into individual modules from the decomposable sentence sets. To validate this microservice-oriented decomposition approach, we performed a case study on Cargo Tracking System that is a typical case decomposed by other microservices identification methods (Service Cutter and API Analysis), and made comparisons in terms of specific coupling and cohesion metrics. The results show that the proposed dataflow-driven decomposition approach can recommend microservice candidates with sound coupling and cohesion through a rigorous and easy-to-operate implementation with semi-automatic support.

Measuring Class Cohesion Using Internal and External Class Relationships

Class cohesion is a design quality that has a great impact on posterior development and maintenance of object-oriented systems. There have been much works on measuring class cohesion based on internal class relationships such as method-attribute referencing and internal method invocations. However, object-oriented systems involve many external class relationships carrying a functional context which should be also taken into account in measuring class cohesion. However, the existing works using internal relationships cannot measure cohesion of classes involving many external relationships. In this paper, we present a novel metric for measuring class cohesion based on both internal and external class relationships through an iterative process. We evaluate the metric by applying it to five applications — ArgoUML, jEdit, JHotDraw, JMeter, and Refactor, and validate the metric theoretically and statistically. The theoretical validation shows that the metric exhibits the required properties for cohesion metrics and the statistical validation shows that the metric captures a new aspect of class cohesion that has not been addressed by the existing works.

A New Metric for Class Cohesion for Object Oriented Software

Various class cohesion metrics exist in literature both at design level and source code level to assess the quality of Object Oriented (OO) software. However, the idea of cohesive interactions (or relationships) between instance variables (i.e., attributes) and methods of a class for measuring cohesion varies from one metric to another. Some authors have used instance variable usage by methods of the class to measure class cohesion while some focus on similarity of methods based on sharing of instance variables. However, researchers believe that such metrics still do not properly capture cohesiveness of classes. Therefore, measures based on different perspective on the idea of cohesive interactions should be developed. Consequently, in this paper, we propose a source code level class cohesion metric based on instance variable usage by methods. We first formalize three types of cohesive interactions and then categorize these cohesive interactions by providing them ranking and weights in order to compute our proposed measure. To determine the usefulness of the proposed measure, theoretical validation using a property based axiomatic framework has been done. For empirical validation, we have used Pearson correlation analysis and logistic regression in an experimental study conducted on 28 Java classes to determine the relationship between the proposed measure and maintenance-effort of classes. The results indicate that the proposed cohesion measure is strongly correlated with maintenance-effort and can serve as a good predictor of the same.

Reusability in multimedia softwares using structural and lexical dependencies

Software reuse focuses on developing software using reusability principles. Component Based Software Engineering (CBSE) is one of the approaches that support software reuse by utilizing reusable software components. Many approaches have been proposed in the literature to mine reusable components from existing object-oriented software. But, almost all of these approaches are dependent on the selection of third-party application software as clients. Therefore, this paper proposes a reusable component identification approach from the source-code of a singular object-oriented software system especially related to multimedia tools. The proposed approach is based on making use of two kinds of dependency relations among different software elements namely structural and lexical relations. These relations, help us to measure cohesion at the component level using two proposed cohesion metrics. The cohesion values are further used as objective functions in the multi-objective search-based evolutionary algorithm, namely NSGA-III to identify the set of reusable components as different clusters. The proposed approach is validated by applying it to six open source Java applications of different sizes and belonging to multimedia domains.

Information-Theoretic Remodularization of Object-Oriented Software Systems

Software remodularization consists in reorganizing software entities into modules such that pairs of entities belonging to the same modules are more similar than those belonging to different modules. In recent years, Search-Based Software Engineering (SBSE) approach has gained unprecedented growth for solving software remodularization problem. Most of the previous studies remodularize the software system by optimizing the structural coupling and cohesion metrics as objective functions. These metrics are defined in terms of the number of structural relationships counts, rather than taking patterns of relationships. It has been observed that the computation of coupling and cohesion based on patterns of relationships (i.e., information-theory based) are more accurate than the number of relationships. This paper proposes an information-theoretic software remodularization where an entropy-based similarity measure is introduced as an objective function along with other objective functions i.e., inter-module class change coupling, intra-module class change coupling, module size index (MSI), and module count index (MCI) and is further optimized using many-objective meta-heuristic algorithm. To evaluate the effectiveness of the proposed approach, seven object-oriented software systems have been remodularized using NSGA-III, MOEA/D, IBEA, and TAA algorithms. The results are compared with existing multi-objective formulation of remodularization problem in terms of authoritative software remodularization, non-extreme distribution, and stability. The experimentation results suggest that the proposed approach can be a good alternative to improve the quality of software systems. The findings suggest that the approach is more suitable for generating remodularization solution good from both quality metrics and developers perspective.

Define, process and describe the intersectoral embedded carbon flow network in China

This article focuses on defining the intersectoral embedded carbon flow network as a matrix to mimic the complex economic-energy-environment symbiotic system in China. We propose a set of synthetical methodologies, which combines life cycle assessment (LCA) and social network analysis (SNA) in the input–output framework. The nodes and relations between nodes in the network are delicately designed such that these relations, which represent the carbon intensity of total intersectoral input between sectors, can be comparable among sectors and over time. Subsequently, based on longitudinal data of input–output tables in China, we derive, sequentialize and dichotomize matrices in order to apply the SNA method to describe the evolution of the intersectoral embedded carbon flow network. The SNA methods used include network visualization, triad census, cohesion metrics, position metrics and core–periphery modeling. Our synthetical methodologies provide a potential systematic solution to carbon reduction in China and help policy makers determine policy priorities rationally.•By constructing an intersectoral embedded carbon flow network matrix, we provide an easily explicable map to aid in the investigation and research in human derived CO2 emissions embedded in the network.•By describing the longitudinal network matrices with SNA, the evolution of the complex economic-energy-environment symbiotic system in China can be mapped out, such as an example illustrated in Wu et al. [1].

There was a CAPSad in the middle of the road: care logic and centrality of the care network for drug users

By applying Social Network Analysis (ARS), this study seeks to evaluate the role of the Center for Psychosocial Care - Alcohol and Other Drugs (CAPSad) in the care network for drug users. It involves an exploratory, cross-sectional and quantitative approach of the Juiz de Fora city network in the state of Minas Gerais. One hundred and eighty-seven care services were identified in the city. The data gathering was made from a questionnaire with professionals of these instruments. The analysis took the cohesion and centrality metrics of the ARS into account as well as the creation of network sociograms. One centrality of the network was found in the CAPSad, in accordance with the policies and the care model advocated in the area, referred to here as "CAPSolization," The centralization in this instrument, still in insufficient number and with structural and workflow dynamics problems, leads to low resilience power of the network indicating the need for care logic modifications, still based on specialized and emergency care, to the detriment of territorial/community and ongoing prospects.

Temporal dynamics of activated sludge bacterial communities in two diversity variant full-scale sewage treatment plants.

Bacterial community in activated sludge (AS) is diverse and highly dynamic. Little is known about the mechanism shaping bacterial community composition and dynamics of AS and no study had quantitatively compared the contribution of abiotic environmental factors and biotic associations to the temporal dynamics of AS microbial communities with significantly different diversity. In this study, two full-scale sewage treatment plants (STPs) with distinct operational parameters and influent composition were sampled biweekly over 1year to reveal the correlating factors to whole and sub-groups of AS bacterial community diversity and dynamics. The results show that the bacterial communities of the two STPs were entirely different and correlated with the influent composition and operating configurations. Bacterial associations represented by cohesion metrics and the environmental factor temperature were the primary correlated factors to the temporal bacterial community dynamics within each STP. The STP with high diversity and evenness could treat influent with higher suspended solid and a shorter sludge retention time, and was less correlated with environmental factors, implying the importance of diversity for AS system.

Size, Coupling and Cohesion Metrics for Product-Line Architecture Evaluation: Proposal and Experimental Validation

Software Product Line (SPL) is an approach for de facto reusing of software artifacts for a given domain. Amongst the artifacts developed in the context of SPL is the Product-Line Architecture (PLA). It is one of the central artifacts of the SPL core assets responsible for abstracting a common architecture for specific products. Organizations should continuously evaluate the quality of their products by managing their PLA evolution and variabilities. Thus, the PLA evaluation should be taken into consideration as one of the most important activities throughout a SPL life cycle. Existing literature presents different PLA evaluation methods, from which metrics are most used. Thus, metrics allow different PLA quality attributes to be prioritized when deriving existing products, most of the time by trading-off them. Size, Coupling and Cohesion are examples of important attributes related to Maintainability from ISO/IEC 25010. Metrics for size, coupling and cohesion have successfully been applied in SPL, however, not taking into account commonalities and variabilities. Therefore, this paper proposes metrics for measuring Size, Coupling and Cohesion of PLA, as well as their experimental validations. Such validations are performed correlating the values collected for each metric to participants rating for each quality attribute. Results indicate a weak positive correlation for the size and coupling metrics and a weak negative correlation for the Cohesion metric. In view of the presented evidence, new studies and/or investigations must be carried out to make results generalizable.

Code refactoring using slice-based cohesion metrics and aspect-oriented programming

Software restructuring is essential for maintaining software quality. It is a usual practice that we first design the software and then go for coding. After coding, if there is any change in the requirement or if the output is incorrect, then we have to modify the code again. For each small code modification, it is not feasible to alter the design. These minor changes made to the code causes decay in the software design. Software refactoring is used to restructure the code to improve the design and quality of the software. In this paper, we propose an approach for performing code refactoring. We use slice-based cohesion metrics to identify the target methods that require refactoring. After identifying the target methods, we use program slicing to divide the target method into two parts. Finally, we use the concept of aspects to alter the code structure in a manner that does not change the external behaviour of the original module.

Recommending Refactoring Solutions Based on Traceability and Code Metrics

Software refactoring has been extensively used to rectify the design flaws and improve software quality without affecting its observable behaviors. For a given code smell, it is common that there exist multiple refactoring solutions. However, it is challenging for developers to select the best one from such potential solutions. Consequently, a number of approaches have been proposed to facilitate the selection. Such approaches compare and select among alternative refactoring solutions based on their impact on metrics of source code. However, their impact on the traceability between source code and requirements is ignored although the importance of such traceability has been well recognized. To this end, we select among alternative refactoring solutions according to how they improve the traceability as well as source code design. To quantify the quality of traceability and source code design we leverage the use of entropy-based and traditional coupling and cohesion metrics respectively. We virtually apply alternative refactoring solutions and measure their effect on the traceability and source code design. The one leading to greatest improvement is recommended. The proposed approach has been evaluated on a well-known data set. The evaluation results suggest that on up to 71% of the cases, developers prefer our recommendation to the traditional recommendation based on code metrics.

Code refactoring using slice-based cohesion metrics and aspect-oriented programming

Code refactoring using slice-based cohesion metrics and aspect-oriented programming

English

English

Ontology Cohesion and Coupling Metrics

Cohesion and coupling metrics for a modular ontology provide an insight into the efficiency of the modularization technique employed. Most of such metrics are either syntax based or do not account for ontology hierarchy. The authors propose cohesion and coupling metrics with continuous scale of measurement for modular ontology via deducing the degree of dependence among ontology components, thereby, handling structural aspects of ontology. The proposed metrics further handle the subtle differences between the type of links. The metrics have been analytically validated using well-established frameworks and the experimental results compared with the existing works, in terms of their cohesion and coupling values as well as the features of ontologies that they can measure. The proposed metrics are unambiguous and can be applied to any type of ontology in order to facilitate the assessment of the quality of an ontology or a partition thereof.

Cohesion: a method for quantifying the connectivity of microbial communities

The ability to predict microbial community dynamics lags behind the quantity of data available in these systems. Most predictive models use only environmental parameters, although a long history of ecological literature suggests that community complexity should also be an informative parameter. Thus, we hypothesize that incorporating information about a community’s complexity might improve predictive power in microbial models. Here, we present a new metric, called community ‘cohesion,’ that quantifies the degree of connectivity of a microbial community. We analyze six long-term (10+ years) microbial data sets using the cohesion metrics and validate our approach using data sets where absolute abundances of taxa are available. As a case study of our metrics’ utility, we show that community cohesion is a strong predictor of Bray–Curtis dissimilarity (R2=0.47) between phytoplankton communities in Lake Mendota, WI, USA. Our cohesion metrics outperform a model built using all available environmental data collected during a long-term sampling program. The result that cohesion corresponds strongly to Bray–Curtis dissimilarity is consistent across the six long-term time series, including five phytoplankton data sets and one bacterial 16S rRNA gene sequencing data set. We explain here the calculation of our cohesion metrics and their potential uses in microbial ecology.