Journal articles: 'Software Metric'

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Kaur, Sandeep. "Software Metrics and Metric Tools A Review." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 4 (2015): 2076–79. http://dx.doi.org/10.17762/ijritcc2321-8169.150468.

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Xiang, Yiming, Weifeng Pan, Haibo Jiang, Yunfang Zhu, and Hao Li. "Measuring Software Modularity Based on Software Networks." Entropy 21, no. 4 (March 28, 2019): 344. http://dx.doi.org/10.3390/e21040344.

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Modularity has been regarded as one of the most important properties of a successful software design. It has significant impact on many external quality attributes such as reusability, maintainability, and understandability. Thus, proposing metrics to measure the software modularity can be very useful. Although several metrics have been proposed to characterize some modularity-related attributes, they fail to characterize software modularity as a whole. A complex network uses network models to abstract the internal structure of complex systems, providing a general way to analyze complex systems as a whole. In this paper, we introduce the complex network theory into software engineering and employ modularity, a metric widely used in the field of community detection in complex network research, to measure software modularity as a whole. First, a specific piece of software is represented by a software network, feature coupling network (FCN), where methods and attributes are nodes, couplings between methods and attributes are edges, and the weight on the edges denotes the coupling strength. Then, modularity is applied to the FCN to measure software modularity. We apply the Weyuker’s criteria which is widely used in the field of software metrics, to validate the modularity as a software metric theoretically, and also perform an empirical evaluation using open-source Java software systems to show its effectiveness as a software metric to measure software modularity.

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Masárová, Renáta. "Fréchet Metric for Space of Binary Coded Software." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 22, no. 35 (December 1, 2014): 17–21. http://dx.doi.org/10.2478/rput-2014-0030.

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Abstract As stated in (7), binary coded computer programs can be shown as a metric space. Therefore, they can be measured by metric in a sense of metric space theory. This paper presents the proof that Fréchet metric is a metric on the space of all sequences of elements M={0,1t} Therefore, it is usable to build a system of software metrics based on the metric space theory

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Kim, Jungho, Sungwon Kang, Jongsun Ahn, and Seonah Lee. "EMSA: Extensibility Metric for Software Architecture." International Journal of Software Engineering and Knowledge Engineering 28, no. 03 (March 2018): 371–405. http://dx.doi.org/10.1142/s0218194018500134.

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Software extensibility, the capability of adding new functions to a software system, is established based on software architecture. Therefore, developers need to evaluate the capability when designing software architecture. To support the evaluation, researchers have proposed metrics based on quality models or scenarios. However, those metrics are vague or subjective, depending on specific systems and evaluators. We propose the extensibility metric for software architecture (EMSA), which represents the degree of extensibility of a software system based on its architecture. To reduce the subjectivity of the metric, we first identify a typical task of adding new functions to a software system. Second, we define the metrics based on the characteristics of software architecture and its changes and finally combine them into a single metric. The originality of EMSA comes from defining metrics based on software architecture and extensibility tasks and integrating them into one. Furthermore, we made an effort to translate the degree into effort estimation expressed as person-hours. To evaluate EMSA, we conducted two types of user studies, obtaining measurements in both a laboratory and a real-world project. The results show that the EMSA estimation is reasonably accurate [6.6% MMRE and 100% PRED(25%)], even in a real-world project (93.2% accuracy and 8.5% standard deviation).

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SHARMA, ARUN, RAJESH KUMAR, and P. S. GROVER. "EMPIRICAL EVALUATION AND VALIDATION OF INTERFACE COMPLEXITY METRICS FOR SOFTWARE COMPONENTS." International Journal of Software Engineering and Knowledge Engineering 18, no. 07 (November 2008): 919–31. http://dx.doi.org/10.1142/s0218194008003957.

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The major drivers for complex applications are cost, efficiency, development time, understandability, usability and more importantly the maintainability. Due to their black box nature, complexity of software components is more crucial for component-based systems. This paper discusses various complexity concerns for these systems and reviews a number of complexity metrics for software components and component-based systems. As interfaces are the only source of information to know about the black-box components, this paper proposes a new interface complexity metric for these components. This metric is based on the information available in the interfaces like interface methods and properties. It also discusses the methodology to assign the weight values to these methods and properties to finally evaluate the complexity of the component. This paper validates the proposed metric against standard Weyukar's properties and empirically evaluates the metric for several Java Bean components. Finally a correlation analysis between proposed metrics and several other metrics like performance, customizability and readability is done to validate the metric.

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Sabbani, Sarachyuth, Kiran Kumar Reddi, and S. V. Achuta Rao. "Software Quality: Issues, Concerns and New Directions." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (November 27, 2013): 2887–94. http://dx.doi.org/10.24297/ijct.v11i8.3007.

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Software metrics and quality models have a very important role to play in measurement of software quality. A number of well-known quality models and software metrics are used to build quality software both in industry and in academia. Development of software metrics is an ongoing process with new metrics being continuously tried out.Â However, during our research on measuring software quality using object oriented design patterns, we faced many issues related to existing software metrics and quality models. For a particular situation of interest, any established metric can be used. If none is found to be appropriate, a new metric can be devised. In this paper, we discuss some of these issues and present our approach to software quality assessment.

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Phani Sheetal, A., and K. Ravindranath. "Software metric evaluation on cloud based applications." International Journal of Engineering & Technology 7, no. 1.5 (December 31, 2017): 13. http://dx.doi.org/10.14419/ijet.v7i1.5.9071.

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Unbound growth in the cloud computing service models have motivated the companies building traditional software to be migrated into the clouds. During the high demand of the traditional applications, the performance and quality of the software were evaluated by the popular and globally accepted metrics. Nevertheless, after the migration of the same applications into the cloud, the expectation and definition of performance and quality has been changed. The beneficiaries of these applications are setting new milestones for the applications. Hence, the recent demand of the research trend is to build new software metric models to match the trade of between the new expectations from the beneficiaries and the software quality policies for organization or individual or state. Thus this work makes an attempt to understand the traditional software quality metrics and try to justify the applicability of these parameters in the trend of cloud based software applications. This work also proposes a novel metric method for performance evaluation for the migrated applications into the cloud, with the intension of formalizing and standardizing the cloud based metric methods unlike the recent trends.

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Srivastava, Varun Kar Lal, and Dr Amit Asthana. "An Efficient Universal Software Metric Tool for C#." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10 (October 31, 2019): 75–81. http://dx.doi.org/10.5373/jardcs/v11i10/20193008.

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CHAN, VICTOR K. Y., W. ERIC WONG, and T. F. XIE. "A STATISTICAL METHODOLOGY TO SIMPLIFY SOFTWARE METRIC MODELS CONSTRUCTED USING INCOMPLETE DATA SAMPLES." International Journal of Software Engineering and Knowledge Engineering 17, no. 06 (December 2007): 689–707. http://dx.doi.org/10.1142/s0218194007003495.

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Software metric models predict the target software metric(s), e.g., the development work effort or defect rates, for any future software project based on the project's predictor software metric(s), e.g., the project team size. Obviously, the construction of such a software metric model makes use of a data sample of such metrics from analogous past projects. However, incomplete data often appear in such data samples. Moreover, the decision on whether a particular predictor metric should be included is most likely based on an intuitive or experience-based assumption that the predictor metric has an impact on the target metric with a statistical significance. However, this assumption is usually not verifiable "retrospectively" after the model is constructed, leading to redundant predictor metric(s) and/or unnecessary predictor metric complexity. To solve all these problems, we derived a methodology consisting of the k-nearest neighbors (k-NN) imputation method, statistical hypothesis testing, and a "goodness-of-fit" criterion. This methodology was tested on software effort metric models and software quality metric models, the latter usually suffers from far more serious incomplete data. This paper documents this methodology and the tests on these two types of software metric models.

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Shao, Yanli, Jingru Zhao, Xingqi Wang, Weiwei Wu, and Jinglong Fang. "Research on Cross-Company Defect Prediction Method to Improve Software Security." Security and Communication Networks 2021 (August 24, 2021): 1–19. http://dx.doi.org/10.1155/2021/5558561.

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As the scale and complexity of software increase, software security issues have become the focus of society. Software defect prediction (SDP) is an important means to assist developers in discovering and repairing potential defects that may endanger software security in advance and improving software security and reliability. Currently, cross-project defect prediction (CPDP) and cross-company defect prediction (CCDP) are widely studied to improve the defect prediction performance, but there are still problems such as inconsistent metrics and large differences in data distribution between source and target projects. Therefore, a new CCDP method based on metric matching and sample weight setting is proposed in this study. First, a clustering-based metric matching method is proposed. The multigranularity metric feature vector is extracted to unify the metric dimension while maximally retaining the information contained in the metrics. Then use metric clustering to eliminate metric redundancy and extract representative metrics through principal component analysis (PCA) to support one-to-one metric matching. This strategy not only solves the metric inconsistent and redundancy problem but also transforms the cross-company heterogeneous defect prediction problem into a homogeneous problem. Second, a sample weight setting method is proposed to transform the source data distribution. Wherein the statistical source sample frequency information is set as an impact factor to increase the weight of source samples that are more similar to the target samples, which improves the data distribution similarity between the source and target projects, thereby building a more accurate prediction model. Finally, after the above two-step processing, some classical machine learning methods are applied to build the prediction model, and 12 project datasets in NASA and PROMISE are used for performance comparison. Experimental results prove that the proposed method has superior prediction performance over other mainstream CCDP methods.

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Грицюк, Ю. І., and Т. О. Муха. "Methods of determination of quality of software." Scientific Bulletin of UNFU 30, no. 1 (February 27, 2020): 158–67. http://dx.doi.org/10.36930/40300127.

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Developed modern software tool for determining the quality of software (SW) techniques metric analysis. The software allows you to use quality metrics to calculate the corresponding metric and determine the value of the complex index of quality software product. Clarified the quality assessment process, software analyzes the concept of the quality of the software product as an object of standardization and quality levels of performance models of the software. This allowed the opportunity to improve the quality of software by generating the relevant requirements of the criteria for quality evaluation. It is also possible to make the improvement of the metric analysis of models of its quality and its quantitative measurement methods in all phases of a software project. It was revealed that the driving force behind the success of software projects is the desire of their leaders to develop such software, which would have a certain value. It should be important for certain tasks or to achieve tactical and strategic objectives. The value of the software can be expressed in the form of its value, or in some other form. The customer usually has their own idea of the maximum cost of investing in the development of software. These funds profit it expects to achieve in the case of the main goals of using the software. It can also have a vision of the functionality of software and certain expectations of its quality. The features of the use of the metric analysis for determining the quality of the software, revealed the lack of uniform standards for the metric. Therefore, each supplier of its measurement system offers its own methods of evaluating the quality of software and associated metrics. Also it is challenging the interpretation of metric values, since for the majority of users of its software metrics and their values are not absolutely clear and informative. It was found that the main parameters of the choice of an embodiment of the software is its cost, the duration of the development process and the reputation of the designer of the company. But the decisions taken on the basis of these parameters, not always guarantee proper quality of the software.

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Boloix, Germinal, and Pierre Germinal. "Interconnectivity Metric For Software Complexity." INFOR: Information Systems and Operational Research 26, no. 1 (August 1988): 17–39. http://dx.doi.org/10.1080/03155986.1988.11732053.

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Shatnawi, Raed, and Alok Mishra. "An Empirical Study on Software Fault Prediction Using Product and Process Metrics." International Journal of Information Technologies and Systems Approach 14, no. 1 (January 2021): 62–78. http://dx.doi.org/10.4018/ijitsa.2021010104.

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Product and process metrics are measured from the development and evolution of software. Metrics are indicators of software fault-proneness and advanced models using machine learning can be provided to the development team to select modules for further inspection. Most fault-proneness classifiers were built from product metrics. However, the inclusion of process metrics adds evolution as a factor to software quality. In this work, the authors propose a process metric measured from the evolution of software to predict fault-proneness in software models. The process metrics measures change-proneness of modules (classes and interfaces). Classifiers are trained and tested for five large open-source systems. Classifiers were built using product metrics alone and using a combination of product and the proposed process metric. The classifiers evaluation shows improvements whenever the process metrics were used. Evolution metrics are correlated with quality of software and helps in improving software quality prediction for future releases.

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Arvanitou, Elvira-Maria, Apostolos Ampatzoglou, Alexander Chatzigeorgiou, and Paris Avgeriou. "Software metrics fluctuation: a property for assisting the metric selection process." Information and Software Technology 72 (April 2016): 110–24. http://dx.doi.org/10.1016/j.infsof.2015.12.010.

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Welzel, Dieter, Hans-Ludwig Hausen, and Jøsrgen Bøsegh. "A metric-based software evaluation method." Software Testing, Verification and Reliability 3, no. 3-4 (September 1993): 181–94. http://dx.doi.org/10.1002/stvr.4370030306.

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Al Dallal, J. "Software similarity-based functional cohesion metric." IET Software 3, no. 1 (2009): 46. http://dx.doi.org/10.1049/iet-sen:20080054.

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Stojiljković, Milan, and Vlado Delic. "Noise Metric ‐ Environmental Noise Analysis Software." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3655. http://dx.doi.org/10.1121/1.2934957.

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WANG, HUANJING, TAGHI M. KHOSHGOFTAAR, JASON VAN HULSE, and KEHAN GAO. "METRIC SELECTION FOR SOFTWARE DEFECT PREDICTION." International Journal of Software Engineering and Knowledge Engineering 21, no. 02 (March 2011): 237–57. http://dx.doi.org/10.1142/s0218194011005256.

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Real-world software systems are becoming larger, more complex, and much more unpredictable. Software systems face many risks in their life cycles. Software practitioners strive to improve software quality by constructing defect prediction models using metric (feature) selection techniques. Finding faulty components in a software system can lead to a more reliable final system and reduce development and maintenance costs. This paper presents an empirical study of six commonly used filter-based software metric rankers and our proposed ensemble technique using rank ordering of the features (mean or median), applied to three large software projects using five commonly used learners. The classification accuracy was evaluated in terms of the AUC (Area Under the ROC (Receiver Operating Characteristic) Curve) performance metric. Results demonstrate that the ensemble technique performed better overall than any individual ranker and also possessed better robustness. The empirical study also shows that variations among rankers, learners and software projects significantly impacted the classification outcomes, and that the ensemble method can smooth out performance.

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Wang, Huanjing, Taghi M. Khoshgoftaar, and Amri Napolitano. "An Empirical Investigation on Wrapper-Based Feature Selection for Predicting Software Quality." International Journal of Software Engineering and Knowledge Engineering 25, no. 01 (February 2015): 93–114. http://dx.doi.org/10.1142/s0218194015400057.

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The basic measurements for software quality control and management are the various project and software metrics collected at various states of a software development life cycle. The software metrics may not all be relevant for predicting the fault proneness of software components, modules, or releases. Thus creating the need for the use of feature (software metric) selection. The goal of feature selection is to find a minimum subset of attributes that can characterize the underlying data with results as well as, or even better than the original data when all available features are considered. As an example of inter-disciplinary research (between data science and software engineering), this study is unique in presenting a large comparative study of wrapper-based feature (or attribute) selection techniques for building defect predictors. In this paper, we investigated thirty wrapper-based feature selection methods to remove irrelevant and redundant software metrics used for building defect predictors. In this study, these thirty wrappers vary based on the choice of search method (Best First or Greedy Stepwise), leaner (Naïve Bayes, Support Vector Machine, and Logistic Regression), and performance metric (Overall Accuracy, Area Under ROC (Receiver Operating Characteristic) Curve, Area Under the Precision-Recall Curve, Best Geometric Mean, and Best Arithmetic Mean) used in the defect prediction model evaluation process. The models are trained using the three learners and evaluated using the five performance metrics. The case study is based on software metrics and defect data collected from a real world software project. The results demonstrate that Best Arithmetic Mean is the best performance metric used within the wrapper. Naïve Bayes performed significantly better than Logistic Regression and Support Vector Machine as a wrapper learner on slightly and less imbalanced datasets. We also recommend Greedy Stepwise as a search method for wrappers. Moreover, comparing to models built with full datasets, the performances of defect prediction models can be improved when metric subsets are selected through a wrapper subset selector.

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Gradišnik, Mitja, Tina Beranič, and Sašo Karakatič. "Impact of Historical Software Metric Changes in Predicting Future Maintainability Trends in Open-Source Software Development." Applied Sciences 10, no. 13 (July 3, 2020): 4624. http://dx.doi.org/10.3390/app10134624.

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Software maintenance is one of the key stages in the software lifecycle and it includes a variety of activities that consume the significant portion of the costs of a software project. Previous research suggest that future software maintainability can be predicted, based on various source code aspects, but most of the research focuses on the prediction based on the present state of the code and ignores its history. While taking the history into account in software maintainability prediction seems intuitive, the research empirically testing this has not been done, and is the main goal of this paper. This paper empirically evaluates the contribution of historical measurements of the Chidamber & Kemerer (C&K) software metrics to software maintainability prediction models. The main contribution of the paper is the building of the prediction models with classification and regression trees and random forest learners in iterations by adding historical measurement data extracted from previous releases gradually. The maintainability prediction models were built based on software metric measurements obtained from real-world open-source software projects. The analysis of the results show that an additional amount of historical metric measurements contributes to the maintainability prediction. Additionally, the study evaluates the contribution of individual C&K software metrics on the performance of maintainability prediction models.

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Najadat, Hassan, Izzat Alsmadi, and Yazan Shboul. "Predicting Software Projects Cost Estimation Based on Mining Historical Data." ISRN Software Engineering 2012 (April 10, 2012): 1–8. http://dx.doi.org/10.5402/2012/823437.

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In this research, a hybrid cost estimation model is proposed to produce a realistic prediction model that takes into consideration software project, product, process, and environmental elements. A cost estimation dataset is built from a large number of open source projects. Those projects are divided into three domains: communication, finance, and game projects. Several data mining techniques are used to classify software projects in terms of their development complexity. Data mining techniques are also used to study association between different software attributes and their relation to cost estimation. Results showed that finance metrics are usually the most complex in terms of code size and some other complexity metrics. Results showed also that games applications have higher values of the SLOCmath, coupling, cyclomatic complexity, and MCDC metrics. Information gain is used in order to evaluate the ability of object-oriented metrics to predict software complexity. MCDC metric is shown to be the first metric in deciding a software project complexity. A software project effort equation is created based on clustering and based on all software projects’ attributes. According to the software metrics weights values developed in this project, we can notice that MCDC, LOC, and cyclomatic complexity of the traditional metrics are still the dominant metrics that affect our classification process, while number of children and depth of inheritance are the dominant from the object-oriented metrics as a second level.

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Zage, Wayne M., Dolores M. Zage, and Cathy Wilburn. "Avoiding metric monsters: A design metrics approach." Annals of Software Engineering 1, no. 1 (December 1995): 43–55. http://dx.doi.org/10.1007/bf02249045.

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Wang, Muchou, Weifeng Pan, Bo Jiang, and Chenxiang Yuan. "CLEAR: Class Level Software Refactoring Using Evolutionary Algorithms." Journal of Intelligent Systems 24, no. 1 (March 1, 2015): 85–97. http://dx.doi.org/10.1515/jisys-2013-0058.

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AbstractThe original design of a software system is rarely prepared for every new requirement. Software systems should be updated frequently, which is usually accompanied by the decline in software modularity and quality. Although many approaches have been proposed to improve the quality of software, a majority of them are guided by metrics defined on the local properties of software. In this article, we propose to use a global metric borrowed from the network science to detect the moving method refactoring. First, our approach uses a bipartite network to represent classes, features (i.e., methods and fields), and their dependencies. Second, a new metric is introduced to quantify the modularity of a software system as a whole. Finally, a crossover-only evolutionary algorithm that uses the metric as its fitness function is introduced to optimize the class structure of a software system and detect the methods that should be moved. Empirical results on the benchmark Java projects show that our approach can find meaningful methods that should be moved with a high stability. The advantages of our approach are illustrated in comparison with some other approaches, specifically one refactoring approach, namely search-based refactoring approach (SBRA), and two community detection algorithms, namely a graph theoretic clustering algorithm (MCODE) and a fast algorithm for community detection (FG). Our approach provides a new way to do refactoring from the perspective of software structure.

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Concas, Giulio, Michele Marchesi, Cristina Monni, Matteo Orrù, and Roberto Tonelli. "Software Quality and Community Structure in Java Software Networks." International Journal of Software Engineering and Knowledge Engineering 27, no. 07 (September 2017): 1063–96. http://dx.doi.org/10.1142/s0218194017500401.

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We present a study of 600 Java software networks with the aim of characterizing the relationship among their defectiveness and community metrics. We analyze the community structure of such networks, defined as their topological division into subnetworks of densely connected nodes. A high density of connections represents a higher level of cooperation between classes, so a well-defined division in communities could indicate that the software system has been designed in a modular fashion and all its functionalities are well separated. We show how the community structure can be an indicator of well-written, high quality code by retrieving the communities of the analyzed systems and by ranking their division in communities through the built-in metric called modularity. We found that the software systems with highest modularity possess the majority of bugs, and tested whether this result is related to some confounding effect. We found two power laws relating the maximum defect density with two different metrics: the number of detected communities inside a software network and the clustering coefficient. We finally found a linear correlation between clustering coefficient and number of communities. Our results can be used to make predictive hypotheses about software defectiveness of future releases of the analyzed systems.

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Gosain, Anjana, and Ganga Sharma. "A New Metric for Class Cohesion for Object Oriented Software." International Arab Journal of Information Technology 17, no. 3 (May 1, 2019): 411–21. http://dx.doi.org/10.34028/iajit/17/3/15.

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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.

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THONGMAK, MATHUPAYAS, and PORNSIRI MUENCHAISRI. "MAINTAINABILITY METRICS FOR ASPECT-ORIENTED SOFTWARE." International Journal of Software Engineering and Knowledge Engineering 19, no. 03 (May 2009): 389–420. http://dx.doi.org/10.1142/s0218194009004234.

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Maintainability is an important factor that developers should be concerned because two-thirds of software costs involve maintenance. Aspect-oriented programming (AOP) paradigm is aimed to increase the software maintainability. It solves code tangling and code scattering problems by introducing a new modular unit, called "aspect". Various research works are provided to support measuring the object-oriented software, but only few studies are set up to support measuring the aspect-oriented software. This paper proposes aspect-oriented software maintainability metrics and a set of aspect-oriented design guidelines to support the metrics. By combining the proposed guidelines, object-oriented design principles, and aspect-oriented design principles, the metrics are constructed according to the Factor-Strategy (FS) quality model and the Factor-Criteria-Metric (FCM) quality model. Principle violation check definitions in the form of Boolean expressions are also defined to conduct software measurement and to fulfill the metrics. Finally, the aspect-oriented software maintainability metrics are applied to detect design principle violations in fifty AspectJ systems. The results show that for all systems their hidden flaws are exposed. Moreover, the proposed metrics are used to compare the maintainability between two versions of systems written in Java and AspectJ.

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Venkitachalam, Hariharan, Christian Granrath, Balachandar Gopalakrishnan, and Johannes Richenhagen. "Metric-based Evaluation of Powertrain Software Architecture." SAE International Journal of Passenger Cars - Electronic and Electrical Systems 10, no. 1 (March 28, 2017): 194–208. http://dx.doi.org/10.4271/2017-01-1615.

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Needham, D. M., and S. A. Jones. "A software fault tree key node metric." Journal of Systems and Software 80, no. 9 (September 2007): 1530–40. http://dx.doi.org/10.1016/j.jss.2007.01.042.

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Pena-Pereira, Francisco, Wojciech Wojnowski, and Marek Tobiszewski. "AGREE—Analytical GREEnness Metric Approach and Software." Analytical Chemistry 92, no. 14 (June 15, 2020): 10076–82. http://dx.doi.org/10.1021/acs.analchem.0c01887.

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Ronald, E. Prather. "Convexity and independence in software metric theory." Software Engineering Journal 11, no. 4 (1996): 238. http://dx.doi.org/10.1049/sej.1996.0029.

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Jatain, Aman. "Metric based reusability analysis of software systems." Journal of Interdisciplinary Mathematics 23, no. 1 (January 2, 2020): 107–16. http://dx.doi.org/10.1080/09720502.2020.1721672.

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Yi, Tong, and Chun Fang. "A complexity metric for object-oriented software." International Journal of Computers and Applications 42, no. 6 (May 30, 2018): 544–49. http://dx.doi.org/10.1080/1206212x.2018.1478194.

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Tiwari, Umesh, and Santosh Kumar. "Cyclomatic complexity metric for component based software." ACM SIGSOFT Software Engineering Notes 39, no. 1 (February 11, 2014): 1–6. http://dx.doi.org/10.1145/2557833.2557853.

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Prather, Ronald E. "The subprogram problem for software metric design." Information Processing Letters 60, no. 3 (November 1996): 143–49. http://dx.doi.org/10.1016/s0020-0190(96)00152-4.

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Jones, Capers. "Function points as a universal software metric." ACM SIGSOFT Software Engineering Notes 38, no. 4 (July 12, 2013): 1–27. http://dx.doi.org/10.1145/2492248.2492268.

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Gruzenkin, D. V., I. A. Yakimov, A. S. Kuznetsov, R. Yu Tsarev, G. V. Grishina, A. N. Pupkov, and N. V. Bystrova. "Algorithm diversity metric for N-version software." Journal of Physics: Conference Series 1333 (October 2019): 032086. http://dx.doi.org/10.1088/1742-6596/1333/3/032086.

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Aggarwal, K. K., Yogesh Singh, and Jitender Kumar Chhabra. "A dynamic software metric and debugging tool." ACM SIGSOFT Software Engineering Notes 28, no. 2 (March 2003): 1. http://dx.doi.org/10.1145/638750.638773.

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Rambo, Rob, Pepper Buckley, and Elmer Branyan. "Establishment and Validation of Software Metric Factors." Journal of Parametrics 5, no. 3 (September 1985): 21–32. http://dx.doi.org/10.1080/10157891.1985.10462658.

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Ebert, Christof. "Classification techniques for metric-based software development." Software Quality Journal 5, no. 4 (December 1996): 255–72. http://dx.doi.org/10.1007/bf00209184.

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Offutt, A. Jefferson, Mary Jean Harrold, and Priyadarshan Kolte. "A software metric system for module coupling." Journal of Systems and Software 20, no. 3 (March 1993): 295–308. http://dx.doi.org/10.1016/0164-1212(93)90072-6.

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Dey, Tapajit, and Audris Mockus. "Deriving a usage-independent software quality metric." Empirical Software Engineering 25, no. 2 (February 19, 2020): 1596–641. http://dx.doi.org/10.1007/s10664-019-09791-w.

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Pinson, Margaret H., Philip J. Corriveau, Mikołaj Leszczuk, and Michael Colligan. "Open Software Framework for Collaborative Development of No Reference Image and Video Quality Metrics." Electronic Imaging 2020, no. 11 (January 26, 2020): 92–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.11.hvei-092.

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This paper describes ongoing work within the video quality experts group (VQEG) to develop no-reference (NR) audiovisual video quality analysis (VQA) metrics. VQEG provides an open forum that encourages knowledge sharing and collaboration. The VQEG no-reference Metric (NORM) group’s goal is to develop open-source NR-VQA metrics that meet industry requirements for scope, accuracy, and capability. This paper presents industry specifications from discussions at VQEG face-to-face meetings among industry, academic, and government participants. This paper also announces an open software framework for collaborative development of NR image quality Analysis (IQA) and VQA metrics <ext-link ext-link-type="url" xlink:href="https://github.com/NTIA/NRMetricFramework"><https://github.com/NTIA/NRMetricFramework></ext-link>. This framework includes the support tools necessary to begin research and avoid common mistakes. VQEG’s goal is to produce a series of NR-VQA metrics with progressively improving scope and accuracy. This work draws upon and enables IQA metric research, as both use the human visual system to analyze the quality of audiovisual media on modern displays. Readers are invited to participate.

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Song, Dong Hun, Yongjin Seo, and Hyeon Soo Kim. "Selection Method of Software Metrics and Metric Tools using Model-Based Selection Criteria." KIISE Transactions on Computing Practices 24, no. 1 (January 31, 2018): 46–52. http://dx.doi.org/10.5626/ktcp.2018.24.1.46.

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Greshnikov, I. I., L. S. Kuravsky, and G. A. Yuriev. "Principles of Developing a Software and Hardware Complex for Crew Intelligent Support and Training Level Assessment." Моделирование и анализ данных 11, no. 2 (2021): 5–30. http://dx.doi.org/10.17759/mda.2021110201.

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Presented is a new approach to aircraft crew intelligent support, which is based on comparing flight fragments (maneuvers) under study with the relevant patterns contained in the database and representing the system “empirical intelligence”. Principal components of this approach are four new metrics for comparing flight fragments, viz.: the Euclidean metric in the space of wavelet coefficients; the likelihood metric of eigenvalue trajectories for transformations of activity parameters; the Kohonen metric in the space of wavelet coefficients; the likelihood metric for comparing gaze trajectories. Features of the presented approach are: the presence of an “intelligent component” that is contained in empirical data and can be flexibly changed as they accumulate; the use of integral comparisons of the flight fragments under study and video oculography data with relevant patterns of various types and performance quality from a specialized database, with transferring characteristics of the nearest pattern from this specialized database to the fragment under study; applying a complex combination of the methods for stochastic processes analysis and multivariate statistical techniques.

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Ardito, Luca, Riccardo Coppola, Luca Barbato, and Diego Verga. "A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review." Scientific Programming 2020 (August 4, 2020): 1–26. http://dx.doi.org/10.1155/2020/8840389.

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Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.

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Suresh, Yeresime, Lov Kumar, and Santanu Ku Rath. "Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis." ISRN Software Engineering 2014 (March 4, 2014): 1–15. http://dx.doi.org/10.1155/2014/251083.

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Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.

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He, Peng, Bing Li, Yutao Ma, and Lulu He. "Using Software Dependency to Bug Prediction." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/869356.

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Software maintenance, especially bug prediction, plays an important role in evaluating software quality and balancing development costs. This study attempts to use several quantitative network metrics to explore their relationships with bug prediction in terms of software dependency. Our work consists of four main steps. First, we constructed software dependency networks regarding five dependency scenes at the class-level granularity. Second, we used a set of nine representative and commonly used metrics—namely, centrality, degree, PageRank, and HITS, as well as modularity—to quantify the importance of each class. Third, we identified how these metrics were related to the proneness and severity of fixed bugs in Tomcat and Ant and determined the extent to which they were related. Finally, the significant metrics were considered as predictors for bug proneness and severity. The result suggests that there is a statistically significant relationship between class’s importance and bug prediction. Furthermore, betweenness centrality and out-degree metric yield an impressive accuracy for bug prediction and test prioritization. The best accuracy of our prediction for bug proneness and bug severity is up to 54.7% and 66.7% (top 50, Tomcat) and 63.8% and 48.7% (top 100, Ant), respectively, within these two cases.

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Adewumi, Misra, and Damaševičius. "A Complexity Metrics Suite for Cascading Style Sheets." Computers 8, no. 3 (July 10, 2019): 54. http://dx.doi.org/10.3390/computers8030054.

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We perform a theoretical and empirical analysis of a set of Cascading Style Sheets (CSS) document complexity metrics. The metrics are validated using a practical framework that demonstrates their viability. The theoretical analysis is performed using the Weyuker’s properties−a widely adopted approach to conducting empirical validations of metrics proposals. The empirical analysis is conducted using visual and statistical analysis of distribution of metric values, Cliff’s delta, Chi-square and Liliefors statistical normality tests, and correlation analysis on our own dataset of CSS documents. The results show that five out of the nine metrics (56%) satisfy Weyuker’s properties except for the Number of Attributes Defined per Rule Block (NADRB) metric, which satisfies six out of nine (67%) properties. In addition, the results from the statistical analysis show good statistical distribution characteristics (only the Number of Extended Rule Blocks (NERB) metric exceeds the rule-of-thumb threshold value of the Cliff’s delta). The correlation between the metric values and the size of the CSS documents is insignificant, suggesting that the presented metrics are indeed complexity rather than size metrics. The practical application of the presented CSS complexity metric suite is to assess the risk of CSS documents. The proposed CSS complexity metrics suite allows identification of CSS files that require immediate attention of software maintenance personnel.

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Dos Santos Barcelos, Mara Regina, Carlos Francisco Simões Gomes, Adriana Manzolillo Sanseverino, and Marcos Dos Santos. "Literature review on software metrics and a New Proposal." Exatas & Engenharias 11, no. 32 (June 22, 2021): 33–59. http://dx.doi.org/10.25242/885x113220212284.

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The use of metrics is important in software development activities as they make it possible to check quality, identify failures and other benefits. The objective of this paper is to propose a new software metric based on a bibliometric study and a literature review on software metrics. The bibliometric research was carried out in the Scopus and Web of Science databases to identify the distribution of articles by year of publication, the main authors, affiliation, country, the most common languages, the types of documents, journals with more publications, areas of knowledge, and the keyword clusters. Twenty-three articles were subsequently selected for reading to compose the literature review. The results of the bibliometric research show that (i) there is no defined core of research; (ii) there is a fluctuation of the number of published articles; (iii) the predominant language is English, and the country with the highest index of publications is the United States; (iv) the main area of knowledge is computer science; (v) in relation to affiliation, Florida Atlantic University stands out; (vi) the journal with the largest number of publications is the Journal of Systems and Software. The literature review showed that many software metrics can be used for different purposes, but most of them are related to code, and none are related to acceptance. As such, a support metric for the software acceptance process is proposed to facilitate the delivery phase of the software product, providing security for the customer and cost savings for the developing company.

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COUNSELL, S., T. HALL, and D. BOWES. "A THEORETICAL AND EMPIRICAL ANALYSIS OF THREE SLICE-BASED METRICS FOR COHESION." International Journal of Software Engineering and Knowledge Engineering 20, no. 05 (August 2010): 609–36. http://dx.doi.org/10.1142/s0218194010004888.

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Sound empirical research suggests that we should analyze software metrics from a theoretical and practical perspective. This paper describes the result of an investigation into the respective merits of two cohesion-based metrics for program slicing. The Tightness and Overlap metrics were those originally proposed by Weiser for the procedural paradigm. We compare and contrast these two metrics with a third metric for the OO paradigm first proposed by Counsell et al. based on Hamming Distance and based on a matrix-based notation. We theoretically validated the three metrics using the properties of Kitchenham and then empirically validated the same three metrics; some revealing properties of the metrics were found as a result. In particular, that the OO-based metric was the most stable of the three; module length was not a confounding factor for the Hamming Distance-based metric; it was however for the two slice-based metrics supporting previous work by Meyers and Binkley. The number of module slices however, was found to be an even stronger influence on the values of the two slice-based metrics, whose near perfect correlation with each other suggests that they may be measuring the same software attribute. We calculated and then compared the three metrics using first, a set of manufactured, pre-determined modules as a preliminary analysis and second, approximately nine thousand functions from the modules of multiple versions of the Barcode system, used previously by Meyers and Binkley in their empirical study. The over-arching message of the research is that a combination of theoretical and empirical analysis can help significantly in comparing the viability and indeed choice of a metric or set of metrics. More specifically, although cohesion is a subjective measure, there are certain properties of a metric that are less desirable than others and it is these 'relative' features that distinguish metrics, make their comparison possible and their value more evident.

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Journal articles on the topic 'Software Metric'

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