Evaluation of Software Reusability Based on Coupling and Cohesion

Scientific missions in the Earth sciences frequently require cost-effective, highly reliable, and easy-to-use software, which can be a challenge for software developers to provide. The NASA Earth Science Enterprise (ESE) spends a significant amount of resources developing software components and other software development artifacts that may also be of value if reused in other projects requiring similar functionality. In general, software reuse is often defined as utilizing existing software artifacts. Software reuse can improve productivity and quality while decreasing the cost of software development, as documented by case studies in the literature. Since large software systems are often the results of the integration of many smaller and sometimes reusable components, ensuring reusability of such software components becomes a necessity. Indeed, designing software components with reusability as a requirement can increase the software reuse potential within a community such as the NASA ESE community. The NASA Earth Science Data Systems (ESDS) Software Reuse Working Group is chartered to oversee the development of a process that will maximize the reuse potential of existing software components while recommending strategies for maximizing the reusability potential of yet-to-be-designed components. As part of this work, two surveys of the Earth science community were conducted. The first was performed in 2004 and distributed among government employees and contractors. A follow-up survey was performed in 2005 and distributed among a wider community, to include members of industry and academia. The surveys were designed to collect information on subjects such as the current software reuse practices of Earth science software developers, why they choose to reuse software, and what perceived barriers prevent them from reusing software. In this paper, we compare the results of these surveys, summarize the observed trends, and discuss the findings. The results are very similar, with the second, larger survey confirming the basic results of the first, smaller survey. The results suggest that reuse of ESE software can drive down the cost and time of system development, increase flexibility and responsiveness of these systems to new technologies and requirements, and increase effective and accountable community participation.

The authors describe their research which improves software reuse by using an automated approach to semantically search for and retrieve reusable software components in large software component repositories and on the World Wide Web (WWW). Using automation and smart (semantic) techniques, their approach speeds up the search and retrieval of reusable software components, while retaining good accuracy, and therefore improves the affordability of software reuse.A program understanding of software components and natural language understanding of user queries was employed. Then the software component descriptions were compared by matching the resulting semantic representations of the user queries to the semantic representations of the software components to search for software components that best match the user queries.A proof of concept system was developed to test the authors' approach. The results of this proof of concept system were compared to human experts, and statistical analysis was performed on the collected experimental data. The results from these experiments demonstrate that this automated semantic‐based approach for software reusable component classification and retrieval is successful when compared to the labor‐intensive results from the experts, thus showing that this approach can significantly benefit software reuse classification and retrieval.

The term Cleaner Production (CP) for Production Companies is contemplated as influential to get sustainable production. CP mainly deals with three R's that is, reuse, reduce, and recycle. For software enterprise, the software reuse plays a pivotal role. Software reuse is a process of producing new products or software from the existing software by updating it. To extract useful information from the existing software data mining comes into light. The algorithms used for software reuse face issues related to maintenance cost, accuracy, and performance. Also, the currently used algorithm does not give accurate results on whether the component of software can be reused. Machine Learning gives the best results to predicate if the given software component is reusable or not. This paper introduces an integrated Random Forest and Gradient Boosting Machine Learning Algorithm (RFGBM) which test the reusability of the given software code considering the object‐oriented parameters such as cohesion, coupling, cyclomatic complexity, bugs, number of children, and depth inheritance tree. Further, the proposed algorithm is compared with J48, AdaBoostM1, LogitBoost, Part, One R, LMT, JRip, DecisionStump algorithms. Performance metrices like accuracy, error rate, Relative Absolute Error, and Mean Absolute Error are improved using RFGBM. This algorithm also utilizes data preprocessing with the help of an unsupervised filter to remove the missing value for efficiency improvement. Proposed algorithm outperforms existing in term of performance parameters.

The large scale application of reuse to support software development is not a new concept. Over twenty years ago, M. D. McIlroy expressed the need for: “… standard catalogues of routines, classified by precision, robustness, time-space performance, size limits, and binding time of parameters.” [McIlroy 68] He also provided insight that is still valid into: “… the kinds of variability necessary in software components, ways of producing useful inventories, types of components that are ripe for such standardization, and methods of instituting pilot production.”McIlroy emphasized the importance of abstraction of common principles in developing these routines, the need for automatic generators, and uniformity of engineering. He also enumerated the degree of parameterization needed for successful reusable software: Choice of precisionChoice of robustness, trading off reliability and time-space performanceChoice of time-space behaviorChoice of algorithmChoice of interfaces with standards for types of error conditionsChoice of storage accessing methodChoice of data structures within a given algorithm or within different related algorithmsIn the utility and tool area, these concerns have been met with stable, understandable and supported reusable software subsystems to support applications development. Examples of this software are collections of abstract data structures, data base management systems, development environments, and network managers. Requirements for new applications that depend on reuse of such general subsystems software are specified in light of the existence of that software. The development can achieve significant productivity gains when the software subsystems are reused.Except in reusing these subsystems, reusable software practice has not had the same level of success in specific application areas, with few exceptions. For domains such as command and control, communications, avionics, and shipboard systems, reusable software must address specific application requirements particular to the given domain. The development of a reusable air traffic control architecture by Thomson-CSF is an example of reuse that addresses the requirements of a specific application. [Andribet 90] The details of the architectures and components developed by Thomson-CSF remain within the company, and may have limited applicability for other organizations that develop similar systems but use alternate design strategies. The Common Ada Missile Packages (CAMP) project has also produced reusable software components, primarily for missile operational software, but this software has seen only limited application. [McNicholl 88]

As the demand for more flexible, adaptable, extensible, and robust Web based enterprise application systems accelerates, adopting new software engineering methodologies and development strategies becomes critical. These strategies must support the construction of enterprise software systems that assemble highly flexible software components written at different times by various developers. Traditional software development strategies and engineering methodologies, which require development of software systems from scratch, fall short in this regard. Component based software engineering offers an attractive alternative for building Web based enterprise application systems. CBSE works by developing and evolving software from selected reusable software components, then assembling them within appropriate software architectures. By promoting the use of software components that commercial vendors or in-house developers build, the component based software development approach promises large scale software reuse.

Measuring the internal quality of source code is one of the traditional goals of making software development into an engineering discipline. Cyclomatic complexity (CC) is an often used source code quality metric, next to source lines of code (SLOC). However, the use of the CC metric is challenged by the repeated claim that CC is redundant with respect to SLOC because of strong linear correlation.We conducted an extensive literature study of the CC/SLOC correlation results. Next, we tested correlation on large Java (17.6 M methods) and C (6.3 M functions) corpora. Our results show that linear correlation between SLOC and CC is only moderate as a result of increasingly high variance. We further observe that aggregating CC and SLOC as well as performing a power transform improves the correlation.Our conclusion is that the observed linear correlation between CC and SLOC of Java methods or C functions is not strong enough to conclude that CC is redundant with SLOC. This conclusion contradicts earlier claims from literature but concurs with the widely accepted practice of measuring of CC next to SLOC. Copyright © 2015 John Wiley & Sons, Ltd.

One of the biggest obstacles to software reuse is the cost involved in evaluating the suitability of possible reusable components. In recent years, code search engines have made significant progres...

Component-based software development (CBSD) is the process of assembling existing software components in an application such that they interact to satisfy a predefined functionality. This approach can potentially be used to reduce software development costs, assemble systems rapidly, and reduce the maintenance overhead. One of the key challenges faced by software developers is to make component-based development (CBD) an efficient and effective approach. Since components are to be reused across various products and product-families, components must be characterized and tested properly. The present paper is a survey paper and firstly, it discusses CBD and related issues that help improving software reuse. Testing of third party components is a very difficult task in the absence a properly characterized software component. Besides improving software reusability, component characterization also provides better understanding of architecture, better retrieval, better usage and better cataloguing. This paper mainly discusses the essence of proper component characterization that ultimately helps the developers in software reuse, which is highly desirable in component-based software development. Further, paper also discusses other benefits of component characterization that are most essential in component-based development.

The demands of SE imply a growing need for using AI to support all aspects of the software development process. This chapter provides insights into the application of knowledge based approaches to the development of agile software development, software product line, software components and architecture. In particular, it presents three research systems that demonstrate the potential benefits of utilising knowledge based approaches to support agile methods. The first system, called SoBA, supports the use of a story card for agile software development; the second system, called .NET designer, provides design rationale for choosing appropriate architectural solutions, and the third system, called RAIS, provides reuse assessment and improvement for designing reusable software components.

Metrics for reusability of java language components

Software reusability is believed to be the key to improving software development productivity, while specification plays an important part in software reuse. From a modern object oriented view, the reusable software components can be grouped into three categories. Procedural components, abstract data types and concrete data objects. Both procedural components and abstract data types can be parameterized in many Object-Oriented languages. Specifications for reuse of these components are discussed in detail in this paper. The reusable software components and their specifications will form a reusable software library, and the information extracted from the specifications forms a knowledge base. Based on the knowledge base and the reusable software library, a new software development paradigm with software reusability can be realized.

Software Reuse effectiveness can be improved by reducing cost and investment. Software reuse costs can be reduced when reusable components are easy to locate, adapt and integrate into new efficient applications. Reuse is the key paradigm for increasing software quality in the software development. This paper focuses on the implementation of software tool with a new integrated classification scheme to make classification build of software components and effective software reuse repositories to facilitate retrieval of software components depending upon user requirements.

This special issue of EMSE comprises four selected papers on program comprehension, an unusually wide-ranging research area within software engineering. It encompasses the human activities of comprehending software, the technologies for supporting the program comprehension, and their evaluation. It is notable for combining aspects of cognitive psychology, tool design, and empirical study of users, tools, and software artifacts. In “High-MCC Functions in the Linux Kernel”, the authors challenge the assumption that functions with high values of McCabe Cyclomatic Complexity (MCC) are likely to be problematic to understand and maintain. They explore the nuances of what a high MCC value might mean, and examine functions from the Linux kernel with high MCC values. They find that often the structure of these functions is found to be easy to understand by users, leading to the conclusion that syntactic measures alone may not be a good metric for understandability. In “Measuring and Modeling Programming Experience”, the authors describe a controlled experiment in assessing how the experience of software developers can best be measured. This is a particularly important problem in program comprehension research, as developer experience is often a key factor that must be explicitly taken into account when studying how programmers perform problem-solving tasks. In “An Empirical Study on the Impact of Static Typing on Software Maintainability”, the authors tackle one of the classic programming language debates: How does static typing affect human software development capabilities? The authors approach this question from an empirical perspective. In a carefully designed experiment, the authors assess the impact of static typing on understanding undocumented code, fixing type errors, and fixing semantic errors. The results show rigorous empirical evidence that static types are indeed beneficial to these activities, except when fixing semantic errors. In “Labeling Source Code with Information Retrieval Methods: An Empirical Study”, the authors address augmenting source code with explanatory labels. Automated techniques for this exist, which are based on, e.g., Vector Space Models, Latent Semantic Indexing (LSI), and latent Dirichlet allocation (LDA). The authors describe an experiment in which such Empir Software Eng (2014) 19:1259–1260 DOI 10.1007/s10664-014-9329-5

Measuring and estimating the reusability of software components are important steps toward finding reusable candidates. Reuse of software components can aid in the reduction of the development cost of new software or maintenance of existing ones. However, assessing the reusability of software is a challenging task. Even if reusable candidates can be identified, developers will need to decide on the reuse strategies, that is, reuse as it is without any modification, or introduce new changes to the reusable candidate to fulfill requirements in the new environment or releases. Thus, the stability of the reusable candidates plays a pivotal role because it will influence the ease of reuse. Assessing and predicting the stability of potential reusable candidates becomes a significant and important step in software reuse. In this research, we propose to leverage on risk of change as a proxy to measure software stability, where the latter is also a proxy measure for software reuse. Based on experiments conducted on 29 open‐source software with different sizes and application types hosted on GitHub, we found that classes with high impact and high risk of change should be avoided from being reused due to their instability, while those classes with low impact and risk of change should be given priority. The proposed work can aid in providing a better understanding of the ease of reuse for software systems and can be used as a tool to assess its overall quality.

Component Based Software Engineering (CBSE) constructs a quality software system by reusing existing components. For the construction of high-quality software system, reusability plays an important role. Software component should be designed and implemented in such a way that many different programs can reuse them. Reuse of software can increase the productivity and quality of software by reducing effort, time and cost which was elapsed in designing and developing reusable software component. In this paper, a Neuro-fuzzy model has been proposed that uses software component design patterns for analysis and Chidamber and Kemerer (CK) metric for evaluation, optimization and categorization of reusability for component based software. The work is divided into 2 phases. In the first phase, analysis and optimization of reusability are empirically evaluated with high precision value using CK metric and unsupervised Self Organizing Map (SOM) Neural Network. In the second phase, reusability is categorized as very low, low, medium, high and very high using a supervised Back propagation Neural Network (BPNN) and fuzzy inference rules applied on CK metric values. The proposed model may help a software designer to evaluate and optimize the reusability of components while designing software to make quality software system.