There are various tools available on internet that help in analyzing social media mentions for brands on social media. software components are adopted/accepted by developers. for each source code property (size, complexity, etc.). A. Symeonidis, Assessing the user-perceived quality of source code com-. ment, Tech. These kind of metrics prove helpful in shaping appropriate practices, while developing a software and its entire life cycle. reuse of classes, we account only for classes that are explic-, one may notice that the import declaration of a child package, is by deﬁnition inherited to the parent package (e.g. These judgments can be expressed into linguistic values, which makes the process more intuitive. reusability score is given by the following equation: for the case of classes and two for the case of packages and, In this section we evaluate our methodology, ually examine certain components that have been assessed by. practice machine learning algorithms, in order to construct a result, several methodologies have been proposed to assess, the reusability of source code components using static analysis, metrics [10, 11, 33], and practically deﬁne, rics is a non-trivial task, and often requires the aid of qual-. In this work, we refrain from expert-based solutions and employ the actual reuse rate of source code components as ground truth for building a reusability estimation model. The error values refer to the deviation of, the predicted reusability score (using each one of the three ap-. ensuring that these components are suitable for reuse. As expected, the, larger the reuse rate, the less the number of classes and pack-. Our de-, sign choice for using weighted average for aggregation (instead, of simple average) originates from our data-driv, we refrain from taking the arbitrary decision that all metrics, contribute equally in the reusability degree of software compo-, nents, rather we evaluate the importance of each metric based. sign, IEEE Transactions on Software Engineering 20 (6) (1994) 476–493. they may include well-written API meth-. tigate this claim and utilize its potential to construct a system, that receives as input the values of static analysis metrics for a, software component and estimates its reusability. Several approaches that use code metrics rely on the aid of experts for defining target quality scores and deriving metric thresholds, leading to results that are highly context-dependent and subjective. This practice is in principle cost-effective, however it may also lead to low quality software products, if the components to be reused exhibit low quality. Absolutely, their natural tendency to write long lines of code which can be intricate, hard to manage and can’t be reused. Ridge, regression computes the reusability score of a certain compo-, nent taking into account all metrics regardless of the prefer-, ibility to the developer or the quality expert to ignore speciﬁc, metrics when calculating the reusability score (could be di, argue that our modeling strategy is more expressiv. tions of the scores for each source code property. the values of all three metrics. The challenge is how to determine the suitable characteristics and sub characteristics … In this work, we proposed a novel software reusability ev, uation approach based on the hypothesis that the extent to which, a software component is reusable is associated with the way, analyzed the one hundred most popular projects included in the, maven registry and used the capabilities of the code search en-, gine AGORA, in order to calculate the reuse rate at class and. We initially build a benchmark dataset, harnessing the power of online repositories to determine the number of reuse occurrences for each component in the dataset. Although several systems have been designed to automate this procedure by recommending components that cover the desired functionality, the reusability of these components is usually not assessed by these systems. score at both class and package levels, as well as the distribu-. metrics, the class appears to be very well decoupled. We hypothesize that it is often more effective to evaluate test suites based on their coverage of system properties than than of structural program elements. Most approaches. Ad hoc code reuse has been practiced from the earliest days of programming. Within an organization, various types of metrics can be applied which includes reuse metrics and software and quality metrics. Section 2 re-. 73–84. Nowadays, developers tend to adopt a component-based software engineering approach, reusing own implementations and/or resorting to third-party source code. Software Reliability is the probability of failure-free software operation for a specified period of time in a specified environment. belongs to a certain property, we apply a hierarchical ev, tion approach in order to compute the ﬁnal reusability score for, gregating the scores of all metrics that belong to this property, (step e) and aggregate the scores at property level into the ﬁnal, ently in the reusability degree of software, we use weighted av-, erage as our aggregation formula. under some realistic reuse scenarios, and thus further validate. For any organization, improving the business performance means performing their software ... characteristics of software components. b) Flexibility : Changes in the software should be easy to make. rather complex and highly coupled with other classes. This is due to the significant need to train software engineers in generally accepted object-oriented principles. Reduce cost: Less effort, and time saving leads to the overall cost reduction. ectiveness of benchmarking approaches de-, erent mechanisms, using heuristics [28, 29] and statistical, benchmarking , in order to build a target reusabil-, orts attempt to derive reusability by set-, erent software projects (e.g. strategy thus there is no evident added value. benchmarking for improving software maintainability, Software Quality, ence on Software Maintenance, Reengineering, and Reverse Engineering, mark data, in: 2010 IEEE International Conference on Software Mainte-. components. In this context, developers tend to adopt a component-based software engineering approach, reusing own implementations and/or resorting to third-party source code. Figure 6 depicts the histogram calculated for the values of, among the three distributions are expected since each project, has its individual characteristics in terms of functionality. In particular, these metrics are based upon measurement theory and are informed by the insights of experienced object-oriented software developers. proposed approach exhibits certain advantages that lie in sev-, the values of all metrics in order to provide a single reusabil-, ity score does not support the interpretability of the reusability, score and thus does not enable certain actionable recommen-, mation regarding all the intermediate levels (metrics and prop-, erties) that inﬂuence the reusability degree of a software com-, ponent and thus enables the direct identiﬁcation of the source, code characteristics that need improvement towards enhancing, reusability. A good code automatically respects and highlights all the decisions regarding its decision. has led to a new reuse paradigm, where online resources can be The proposed metrics are formally evaluated against a widelyaccepted list of software metric evaluation criteria. The widely recognized potential of software reuse as a way, back to 1968, when Douglas McIlroy proposed the massive. on Source Code Analysis and Manipulation (SCAM), IEEE, 2014, pp. characteristics and sub characteristics for software component reusability for CBSD. The approach of Fazal et al. Software is developed or engineered Software does “wear out”. Software reusability is an attribute in which software or its module is reused with very little or no modification. Software metrics It offers software development and consulting services for cloud computing, enterprise mobility, big data and analytics, user experience and digital commerce. can be the ones proposed by QMOOD model . To do so, we have analyzed the most popular projects included in the maven registry and have computed a large number of static analysis metrics at both class and package levels using SourceMeter tool  that quantify six major source code properties: complexity, cohesion, coupling, inheritance, documentation and size. The widely adopted component-based development paradigm considers the reuse of proper software components as a primary criterion for successful software development. these trends will not be too diverse among projects. Tracking images on social media with Salesforce AI, Salesforce Einstein – Bringing the power of AI to CRM, Best Salesforce tips to simplify the life of enterprises. Figure 2. presents an example of the reuse rate calculation. An example query for a component is shown in Figure 1. A reusable software component, or RSC, is a software entity intended for reuse. Obviously, the manual examination of source code can be very tedious, or even impossible for large and complex software projects and/or projects that change on a regular basis. that our previous work lies in a promising direction [27, 28, 29]. lustrate how one can determine the preference, velopers for source code components at di, we are able to build models capable of estimating the reusabil-, ity of a component as perceived by software dev. Contemporary approaches assess reusability using static analysis metrics by relying on the help of experts, who usually set metric thresholds or provide ground truth values so that estimation models are built. Then, we employ the reuse, rate information in order to assign a certain reusability score to, each bin (step c), this way formulating our ground truth upon, which we train our reusability evaluation models using polyno-. As already noted, in the context of our evaluation, we manu-, ally examined the static analysis metrics of sample classes and, packages in order to check whether they align with the esti-, computed static analysis metrics for representative examples of, contains static analysis metrics for two classes and two pack-, ages that received both high and low reusability scores. Our methodology employs two models: a one-class classifier, used to rule out low quality code, and a neural network, that computes a quality score for each software component. 254–263. Preliminary evaluation indicates that our approach can effectively estimate software quality.  F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. W, derplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, ... • Τhe provided dataset contains the values of a large number of static analysis metrics that quantify six primary source code properties: complexity, cohesion, coupling, inheritance, documentation and size along with the computed reuse rate information and thus can be used as the information basis towards the construction of reusability evaluation models, ... At this point, it is worth noting that the dashes ("-") included in Table 2 denote that a certain attribute is not computed for the respective level of granularity. Preliminary evaluation indicates that our approach effectively estimates software quality at both class and package levels. tion mining, IEEE Transactions on Software Engineering 38 (1) (2012), work for measuring and evaluating program source code quality. Software Engineering (RAISE), Gothenburg, Sweden, 2018, pp. our analysis, which is presented in the following subsections. As a, result, the introduced biases are limited to the ones that origi-, nate from existing trends in the software development ecosys-. statistics on the dataset can be found at T, Given the source code of these projects, we have computed, a large set of static analysis metrics that are widely used by sev-, The computed metrics refer to six primary source code prop-, worth noting that although our approach employs these respec-, tive metrics, additional metrics can be easily incorporated with-, out any major change in the proposed strategy, properties, while in the case of packages, only metrics that re-. i and iii.… Most of them, however, rely on the aid of experts for defining target quality scores and deriving metric thresholds, leading to results that are context-dependent and subjective. on six source code properties, those of complexity, cohesion. Each bin has been assigned with a reusability score based, on the reuse rate of the classes contained in the benchmark. Software reuse is a promising technology in software development and is seen as a solution to the rising cost of software and the existing bottleneck problems due to demands exceeding supplies. For example, statement coverage metrics measure how many and how often lines of code were executed. practice machine learning algorithms, in order to construct the reusability of source code components using static analysis metrics [10, 11, 33], and practically deﬁne reusability metrics using known quality attributes [34, 35]. Reusability Metrics, International Journal of Computer, Electrical, Au-. It differs from hardware reliability in that it reflects the design … This activity's inputs are requirement specification and system requirement specification, and output is a function analysis specification including function comparison table. The extent to which the software can be used by many different users C. The capability of the software to be moved to a different platform D. Which of the following characteristics is primarily associated with software reusability? However, even when expert help is available, it may still be subjective or case-specific. The evaluation of our methodology indicates that our system can effectively assess reusability as perceived by developers. The indexes, of the source code components in both ﬁgures are sorted in as-, depends on the scores that were computed for six source code, Figure 13: Overview of how the individual quality scores per property (dashed.  proposed a metrics-based, reusability scorer that calculates the degree of reusability of, software components based on the values of eight static anal-, ysis metrics. both properties are linked, and thus evaluated, with only one, metric (LCOM5 in the case of cohesion and DIT in the case, accept only distinct values which restricts the reusability ev, est deviation from the ﬁnal score. Most of them, however, rely on the aid of experts for defining target quality scores and deriving metric thresholds, leading to results that are context-dependent and subjective. In ad-, dition, one can use the ﬁndings regarding the importance of the, ware components or enhance them by applying the described, ogy and the fact that we train one model for each metric, the, proposed reusability evaluation approach provides se, ﬁguration abilities that can serve the individual needs of soft-, ware projects. However, programmers must store the code components and knowledge that can be reused, which in return can save time for developing a software requiring similar code capabilities. The analysis was performed in both class and package levels. production of software using reusable components . The import ﬁeld of AGORA is analyzed and split into tokens, ﬁnding all possible declarations of the sample XObject compo-. ponents using static analysis metrics, in: A. Symeonidis, User-perceived reusability estimation based on analy-, Machine Learning Techniques for Software Quality Ev. ware components, International Journal of Computer Science 1 (4) (2006). The index allo. quality of software components is of vital importance. incorporate examples of properly written source code. *” includes not only the package, “org.example.mypackage” but also the package “org.e, Therefore, in order to eliminate the impact of this issue to the, purity in our dataset, we performed a post-processing step that, Table 2: Overview of the computed Static Analysis Metrics, // ReuseRate of android.telephony package += 1, // ReuseRate of android.content package += 1, Figure 2: Example of Reuse rate calculation at class and package level, values of the reuse rate for both classes and packages. 243–252. for the fact that our approach uses more metrics, the main di, ence of the two approaches relies in the fact that our approach, uses the reuse rate as ground truth information and thus pro-. Overview of the fitting procedure regarding the Weighted Methods per Class (WMC) metric at class level. Objectoriented programming helps in achieving the concept of reusability through different types of inheritance programs, which further help in developing reusable software modules. Software reusability also enhances quality and productivity. Many believe software reusability provides the key to tremendous benefits and saving in software development product. Consequently, more and more developers started embedding properties in code. Software reuse is the process of using the existing software to create a new software system. ﬁrst package has a score of 89.3% and the second has 9.68%. Being a customer-focused and technology-driven company, it always helps clients in crafting holistic business value for their software development efforts. The software reusability research started during 1960 and it has a long way to go. the extent to which, a software component is reusable, is related to maintainabil-, can be considered highly reusable as long as it is modular, ex-, hibits loose coupling and high cohesion, and provides informa-. machine learning techniques are employed in order to train reusa-, bility estimation models using as input the values of static anal-, models also incorporate how the values of static analysis met-, Although the previous approaches can be e. tain cases, their applicability in real-world scenarios is limited. curity (QRS), Vienna, Austria, 2016, pp. Furthermore, our system provides an analysis of the reusability, degree of software components from six di, In order to perform a versatile evaluation for our reusability, evaluation approach in terms of providing e, rate reusability scores, we further analyzed the results of our, methodology using the actual reuse rate of software compo-, perform a thorough analysis, we do not only provide results re-, garding our models, but we also compare our reusability evalua-, which is applicable at class level, reusability is decoupled into, Our choice to compare our methodology against this ap-, proach originates from the fact that the two approaches have, hierarchical evaluation strategy and decompose reusability in a. set of properties that correspond to the primary evaluation axes. by personal preference or trend (especially the number of stars). Neural networks are used to estimate the final quality score given metrics from all of these axes. Adopting these reuse-oriented practices has important beneﬁts, troduction of even more code hosting services (as those outlined, above), question-answering communities (e.g. most, Figure 11: Overview of the ﬁtting procedure regarding the Weighted Methods, Figure 12: Overview of the ﬁtting procedure regarding the Weighted Methods, Figures 13 and 14 depict how the individual quality scores, of the source code properties (dashed lines) are aggregated into, one ﬁnal reusability score (solid black line) at class and package, index and the y-axis refers to the reusability score. An interesting idea, would also be the investigation of an adapti, static analysis metrics in the analysis in order to cover addi-, tional aspects of the source code or better quantify the ones that. What is a reusable software component (RSC)? Although tute the information basis upon which our models are built. Expert-Based Software Quality Estimation., in: mance evaluation of reusable software components based neural network, International Journal of Research in Engineering and Technology 1 (2), Model for Procedure-Based Software Systems, International Journal of. In this work we argue that the popularity of software components, as perceived by developers, can be considered as an indicator of software quality. Although these e, not actually provide an interpretable analysis to the developer, ties that need improvement. Software Engineering, IEEE Softw. Upon removing outliers with a one-class classifier, we employ Principal Feature Analysis and examine the semantics among metrics to provide an analysis on five axes for source code components (classes or packages): complexity, coupling, size, degree of inheritance, and quality of documentation. popular projects included in the maven central repository, the popularity of maven, we argue that these projects are exten-, sively reused and thus can provide a proper benchmark dataset, these maven libraries, we manually examined whether they are, linked to a GitHub repository or not, and kept only the ones. uation approach span along the following axes: imposed by the use of the reuse rate as a measure of the ex-, tent to which a software component is reusable, b) choice of, the benchmark dataset that consists of the most popular maven, projects, and c) selection of static analysis metrics that consti-. 21–27. This framework is utilized by architects to ensure that most profitability and consistency are accomplished all through the product advancement life cycle. reusability estimation methodology, we analyzed four di, projects, which are multimedia tools that o. ﬂected in the values of the number of GitHub stars and forks. Chapters 18 and 19 discuss two reuse technologies in more details: components and services. Being an evolving concept, lot of organizations are moving forward to incorporate software reusability within their environment. mechanism for calculating the ﬁnal reusability score. The subjectivity that underlies the notion of quality does not allow the design and development of a universally accepted mechanism for software quality assessment. the manual examination of source code can be very tedious, or, even impossible for large and complex softw, ing demands both in terms of functional and, manual examination of source code, a common practice in-, volves creating a benchmarking code repository using represen-, tative software projects [22, 23, 24, 25, 26, 36] and then apply-, ing machine learning techniques to derive thresholds and deﬁne. In this context, developers tend to adopt a component-based software engineering approach, reusing own implementations and/or resorting to third-party source code. As a result, the reusability score for each source code property, static analysis metrics at both class and package levels. into one complexity score (solid black line line). Vol.76. Communications in Computer and Information Science. Reusability can be achieved by opting for software metrics technique. Reengineering and Reverse Engineering, IEEE, 2014, pp. Reuse is already a common technique employed in other engineering disciplines. 65 projects with more than 3 million lines of code. section, we provide insights on how our methodology and, the respective results can be exploited by the community, First of all, given that our benchmark dataset is composed, of a large set of general purpose libraries and covers a wide, range of reuse scenarios, our reusability evaluation models can, ity degree of software artifacts on the basis of certain source, in order to optimize the ranking of the results based on the, cases where reusability should be calculated based on speciﬁc, metrics given the individual characteristics of the project un-, der evaluation, our methodology is still applicable simply by, changing the benchmark repository with a di, cludes projects that share the same characteristics with the one, scribed in section 3 in order to generate the new models. in more classes in order to regain high cohesion. end, for each property, we aggregate the reusability scores of. GitHub stars and forks, which indicate the extent to which The complete list of metrics along with their associated source code property and their level of computation are shown in, Research on Continuous Implicit Authentication using hand gestures, https://www.seaf-h2020.eu/ An important challenge in this context is to evaluate the degree to which a software component is suitable for reuse, i.e. bins and bin size) that result in minimum information loss. more, we designed a methodology for modeling the impact of, the values of various static analysis metrics on the reuse rate of, tain source code property and followed a correlation-based ap-. Software reusability includes the reuse of any software artifacts in various formats. we design a reusability scoring mechanism for evaluating both. This is why contemporary research is now focused on seeking mechanisms able to produce software quality models that can be easily adjusted to custom user needs. squares routine was selected as the solver function . poses a major challenge for the research community. rely on expert help or employ arbitrary ground truth datasets. 3.1.1 Activity 1-1. Reuse metrics All rights reserved. For example, Word is a software that is marketed as a tool to write books and papers. An appropriate and simple documentation on how to use component can make it more understandable and easy to implement. Fi-, the reusability degree of classes and packages as perceived by, Future work on our methodology can be performed in mul-, proach so as to evaluate the reusability degree of software at, method level and thus expand its applicability in more reuse, corporate additional metrics that originate from the software de-, velopment process and from online repositories (i.e. Upon removing outliers with a one-class classifier, we employ Principal Feature Analysis and examine the semantics among metrics to provide an analysis on five axes for source code components (classes or packages): complexity, coupling, size, degree of inheritance, and quality of documentation. Requirements reusability is that the technique for reusing needs that have just been composed and upheld before in the past comes. Figure 9: Comparative evaluation results. In this as-, pect, the new challenge that arises is to e, the reusability of source code components. Various Revision Characteristics of software are :-. c) Extensibility : It should be easy to increase the functions performed by it. The rest of this paper is organized as follows. In this paper, we define and ity assessment environment for component-based software systems, in: Intern. Software Technologies (ICSOFT), Madrid, Spain, 2017, pp. Engineering, IEEE Press, 2012, pp. Collect Requirement. In this context we have generated a dataset that harnesses information residing in online code hosting facilities and introduces the actual reuse rate of software components as a measure of their reusability. Software can be custom built. ularity of source code components and their reusability. This ensures that the constructed models are e. cient between the values of each metric and the reuse rate. research has shown that highly rated projects (i.e. The requirements specification, the architectural design, and the source code are all software in different formats. ponents (classes or packages), which allows us to produce a. generic distribution that reﬂects our entire benchmark dataset. GitHub stars and forks, which indicate the extent to which already mentioned, however, this analogy also has its limita-, tions; GitHub stars and forks indeed provide an indication of the, reusability for components, however they may be easily sk, by personal preference or trend, while they also cannot o, In this work, we design a more thorough methodology, which, is based not on arbitrary popularity-based metrics, but rather on. Fuzzy multi-criteria decision-making is employed in order to model the uncertainty imposed by experts’ judgments. Ridge Regression  was used for train-, ing the regression model, while the dedicated regularized least-. Component reuse programs. WMC at class level) to a reusability score in, scribed in the previous subsection), we select all compo-, nents (classes or packages) for which the values of the, center), which is computed as the sum of the reuse rates, of the components of the bins that were selected in the, previous step. While developing a complex software, programmers or software developers require immense knowledge and brain-storming. One could argue that the accuracy of the Ridge Regression, modeling approach seems to be comparable to our modeling. In this work, we build a mechanism that employs static analysis metrics extracted from GitHub projects and defines a target quality score based on repositories’ stars and forks, which indicate their adoption/acceptance by developers. Preliminary evaluation of our methodology indicates that our benchmark dataset, harnessing the power of online repositories to determine the number of reuse occurrences for each component, The introduction of the open-source software development, initiative has changed the way software is de, velop their software projects as open-source and store them on-, led to the introduction of an agile, component-based software, engineering paradigm, where developers rely more and more, on reusing existing components in their source code, either in, the form of libraries or simply by copying and integrating useful. Reuse of software or other artifacts that are produced in the course of a software development project, may lead to considerable productivity improvements and, consequently, cost savings. ness the power of open-source code hosting facilities and code, search engines to ﬁnd out the extent to which individual compo-, nents are actually reused, and, most importantly, to determine, what are the quality characteristics that inﬂuence the reuse rate, case comprises the reuse rate of each component, or, as we may, name it, the reusability as perceived by developers (de, propose a methodology for decomposing the inﬂuence of vari-, ous static analysis metrics on the reusability degree of software, and package level and model their behavior to translate their, values into a reusability degree. People interested in software reuse because they want to build systems that are large, reliable, less expensive, more complex and delivered on … The assessment depends on whether the values of, the metrics for each component exceed certain thresholds, as, addition, the calculation of the proposed reusability score as-, sumes that each one of the eight static analysis metrics involv, in the calculation equally contributes in the reusability degree, of software, which is not the case especially for metrics that, Since using predeﬁned metric thresholds is also typically, limited by expert knowledge and may not be applicable on the, plication), several approaches have been proposed to o. the necessity of using predeﬁned thresholds [16, 17, 18, 19. reuse-related information such as reuse frequency . Furthermore, most approaches are, either based on expert-deﬁned metric thresholds [12, 14, 15] or, Preprint submitted to Journal of Systems and Softwar, require some type of ground truth quality score (again deﬁned, by an expert) that can be used to train a model to produce adapt-. all properties seem to be in line with the ﬁnal score. It may be the design, the code, or some other product of the software development process. the results to determine if the import declarations actually cor-, respond to reuses of the component at hand, we maintain two, statistics: (a) the number of source code ﬁles in which the com-, ponent is being reused, and (b) the number of projects in which, Figure 1: AGORA query on import declarations, Although these statistics may seem simplistic, they are ac-, tually quite indicative of the reusability degree of source code, deem it as reusable and therefore choose to use it in their own, nents that are reused multiple times within each project (and, a lot of times in total) are most probably designed exactly for, that type of usage (e.g. In this section, we design a reusability modeling methodol-, ogy based on the reuse rate of source code components as well, As already mentioned, our methodology is based on em-, ploying the reuse rate of software components in order to esti-, mate their reusability as perceived by software de, deﬁne the reuse rate of a software component as the extent to, reuse rate of a component is to ﬁnd out how many other com-, In order to conduct such a measurement, we decided to use, the capabilities of our own code search engine, named AGORA, aware capabilities and has an open API that allows performing. potential of source code residing in open software repositories views current approaches on quality and reusability estimation. This is going to be a bit long answer since this is a vast topic and I will also point out the difference of using Reusability during software and hardware coding. By reusing existing software, developers can create more complex software in a shorter amount of time. validate it and ensure that there are no biases in our models. Join ResearchGate to find the people and research you need to help your work. We present a generic methodology that relates quality with source code metrics and estimates the quality of software components residing in popular GitHub repositories. static analysis metrics into an interpretable reusability score. oue, Identifying source code reuse across repositories using lcs-based, source code similarity, in: IEEE 14th International Working Conference. Although Figure 10: Error distribution of the reusability scores. tion hiding and separation of concerns .
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