Diagram Metrics Research Articles

Revolutionizing JIRA Management with Artificial Intelligence: Streamlining Workflow Efficiency and Enhancing Project Outcomes

IT organizations are investing heavily on artificial intelligence (AI), though the value realization and areas of application of AI are still limited and not explored completely. Artificial intelligence applications are using large set of data to train the system to either generate new content or to predict the outcomes. Predictive analytics is used in agile management projects for budgeting, forecasting and capacity management. The article represents the use of cycle time and cumulative flow diagram metrics in ActionableAgile or JIRA to predict in agile development projects for forecasting of inflow of work and for the improvement of current work processes.

A NONLINEAR REGRESSION MODEL FOR ESTIMATING THE SIZE OF WEB APPLICATIONS CREATED USING SYMFONY FRAMEWORK

The issue of estimating the size of the software in the first stages of work on the project is important, because the obtained size can be used to predict the cost, duration and labor intensity. The share of web applications among all software projects developed today is significant, as is the popularity of programming languages ​​used in web development. The leader in frequency of use is the PHP language with its numerous frameworks. The purpose of the study is to build a non-linear regression model for estimating the size of web applications created using the Symfony PHP framework, using a normalizing decimal logarithm transformation. To build the model, 50 web application projects were found on the GitHub platform, which were created using the Symfony framework. Using the PhpMetrics tool, the following class diagram metrics for these projects were obtained: the number of classes, the average number of methods per class, the depth of the inheritance tree. This choice of metrics is due to the possibility of obtaining such information about the project under development at the early stages of design and the absence of multicollinearity among the selected metrics. Empirical data were tested for normality. The check showed that the data are non-Gaussian, so they were normalized using a decimal logarithm, an outlier check was performed, the outliers were eliminated, and for the remaining data, a linear regression model was built for the normalized data and a nonlinear one for the original data. For comparison, a linear regression model was also constructed for the empirical data under the assumption of normal distribution. The comparison of the models showed a significant improvement in the quality of estimating the size of web applications created using the Symfony framework when applying non-linear regression analysis.

Construction and Validation of Early Software Size Estimation Models Based on ADAF-Adjusted ACD Metrics

Abstract Software size estimation is a vital activity of software project planning and management. Early software size estimation is a challenging task due to the limited information available during the early phases of software development. The goal of this paper is to construct and validate early software size estimation models based on four analysis-to-design adjustment factor (ADAF)-adjusted analysis class diagram metrics (i.e. ADAF-adjusted number of classes, ADAF-adjusted number of attributes, ADAF-adjusted number of methods and ADAF-adjusted number of relationships) using stepwise multiple linear regression and leave-one-out cross-validation. Furthermore, the prediction accuracy of the best-performing proposed model is also compared with the model based on objective class points. The results of this comparison reveal that our proposed method reduces errors significantly (i.e. on average, 16% reduction in mean absolute residual and 24% reduction in mean squared error).

Composition of UML Class Diagrams Using Category Theory and External Constraints

In large software development projects, there is always a need for refactoring and optimization of the design. Usually software designs are represented using UML diagrams (e.g., class diagrams). A software engineering team may create multiple versions of class diagrams satisfying some external constraints. In some cases, subdiagrams of the developed diagrams can be selected and combined into one diagram. It is difficult to perform this task manually since the manual process is very time consuming, is prone to human errors, and is not manageable for large projects. In this paper, we present algorithmic support for automating the generation of composing diagrams, where the composed diagram satisfies a given collection of external constraints and is optimal with respect to a given objective function. The composition of diagrams is based on the colimit operation from category theory. The developed approach was verified experimentally by generating random external constraints (expressed in SPARQL and OWL), generating random class diagrams using these external constraints, generating composed diagrams that satisfy these external constraints and computing class diagram metrics for each composed diagram.

Improving the Accuracy of Early Software Size Estimation Using Analysis-to-Design Adjustment Factors (ADAFs)

Early software size estimation is a challenging task since limited information is available at the time of project inception. Additional information, however, is gradually added as development progresses. The goal of this research is to quantitatively capture the impact on early software size estimation of this additional information introduced especially when transitioning from the analysis phase to the design phase by comparing the analysis class diagram (ACD) and the design class diagram (DCD). We introduce a new class of metrics called analysis-to-design adjustment factors (ADAFs) to accomplish this goal. ADAFs are calculated for four different class diagram metrics - number of classes (NOC), number of attributes (NOA), number of methods (NOM), and number of relationships (NOR) - used in different class diagram-based software size estimation models. We use practical, theoretical, and empirical validation methods to evaluate the applicability of these ADAFs. To assess the utility of these ADAFs in early software size estimation, we compare the accuracy of existing early software size estimation models before and after the application of ADAFs. Results indicate a marked improvement in the accuracy of these models after the application of ADAFs. Furthermore, regression-based models employing problem domain metrics have also been built to predict these ADAFs. All of these models are statistically significant (p-values <; 0.05) with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values between 0.42 and 0.88.

UML class diagram object-oriented metrics: algorithms of calculation

The paper proposes algorithms of the object oriented UML class diagram metrics calculation. Metrics include Average CBO, Average DIT, Average NOC, Average DAC, Average NLM, Average NOM, DAC2, SIZE2, and DSC. The proposed algorithms have become a part of the UML Refactoring tool providing UML class diagram analysis and transformation.

Empirical Analysis of Metrics Using UML Class Diagram

Lots of Organizations before they are setup survey the maintainability of programming frameworks. To give quality program design there exists a critical strategy called Object-Oriented Framework. Object–Oriented estimations might be utilized to study the judgment skills suite of a class diagram’s structure particularly programming valuations and how the models have been developed and portrayed. The UML Class Diagram metrics maintain the Object-Oriented software. It is maintained through the investigation of the association among object oriented metrics and maintainability. This paper shows the effects of a scientific evaluation of software maintainability forecast and metrics. The research aims at the software quality attribute of maintainability as opposite to the method of software maintenance. It also aims to find out the vital correlation between structural complexity metrics and maintenance time. Several investigators have done copious in it, got lots of theoretical outcomes, and subsequently established a chain of practical uses. Due to dynamic changes in object-oriented technology, in today’s scenario the class diagram is an essential UML model, as, researcher must first get to know the use of software in a scientific manner. It is an affordable strategy which has had an exceptional result in recent times. This paper is related to UML class diagram metrics through which a way is provided to maintain UML class diagram complexity weights. UML Class diagram’s qualities will efficiently and technically show the complexity of Object -Oriented Software. A more specific research study has shown that the technique is associated with individual’s experience and also can be useful to improve software quality.

Assessing Software Maintainability Based on Class Diagram Design: A Preliminary Case Study

 Abstract—Can software maintainability be assessed at the early design stage? For a preliminary answer, we conducted a case study. The study adopts a set of metrics for class diagram measurement, and defines three indices for maintainability assessment from the defect-correction perspectives. The dataset under investigation includes the defect repository and corrective maintenance history of Apache Tomcat (maintained from 2006 to 2014). Statistical findings show that some class diagram metrics (such as the number of class association across packages, the number of classes, the inheritance depth for a class et al.) are significantly correlated with the maintainability assessment in this software. The result can guide maintenance-oriented software design, and also motivates us to do a stronger empirical evaluation. 

On the Application of Information Entropy-based Multi-attribute Decision in UML Class Diagram Metrics

The research, development and applications of software measurement have been carried out for more than forty years. Many researchers have done much in it, obtained lots of theoretical results, and developed a series of practical applications. At present, with the rapid development of object-oriented technology used in software theories and application, how to measure software in an effectively and scientifically way is becoming a hot and difficult issue. Class diagram as the most important UML model, its reasonable design has a remarkable effect on the ultimate system. This paper applies information entropy-based multi-attribute decision in UML class diagram metrics, providing a method to measure UML class diagram complexity weights. Its attributes out of information entropy and attributes’ weigh vectors can measure the complexity of objectoriented software effectively and scientifically. Besides, case analysis can prove this metric’s usability. The more precise experiment outcome has proved that the method is connected to human’s experience, and also can be applied to improve software quality.

Source code size estimation approaches for object-oriented systems from UML class diagrams: A comparative study

BackgroundSource code size in terms of SLOC (source lines of code) is the input of many parametric software effort estimation models. However, it is unavailable at the early phase of software development. ObjectiveWe investigate the accuracy of early SLOC estimation approaches for an object-oriented system using the information collected from its UML class diagram available at the early software development phase. MethodWe use different modeling techniques to build the prediction models for investigating the accuracy of six types of metrics to estimate SLOC. The used techniques include linear models, non-linear models, rule/tree-based models, and instance-based models. The investigated metrics are class diagram metrics, predictive object points, object-oriented project size metric, fast&&serious class points, objective class points, and object-oriented function points. ResultsBased on 100 open-source Java systems, we find that the prediction model built using object-oriented project size metric and ordinary least square regression with a logarithmic transformation achieves the highest accuracy (mean MMRE=0.19 and mean Pred(25)=0.74). ConclusionWe should use object-oriented project size metric and ordinary least square regression with a logarithmic transformation to build a simple, accurate, and comprehensible SLOC estimation model.

Characterizing and understanding radiation budget biases in CMIP3/CMIP5 GCMs, contemporary GCM, and reanalysis

AbstractWe evaluate the annual mean radiative shortwave flux downward at the surface (RSDS) and reflected shortwave (RSUT) and radiative longwave flux upward at top of atmosphere (RLUT) from the twentieth century Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 3 (CMIP3) simulations as well as from the NASA GEOS5 model and Modern‐Era Retrospective Analysis for Research and Applications analysis. The results show that a majority of the models have significant regional biases in the annual means of RSDS, RLUT, and RSUT, with biases from −30 to 30 W m−2. While the global average CMIP5 ensemble mean biases of RSDS, RLUT, and RSUT are reduced compared to CMIP3 by about 32% (e.g., −6.9 to 2.5 W m−2), 43%, and 56%, respectively. This reduction arises from a more complete cancellation of the pervasive negative biases over ocean and newly larger positive biases over land. In fact, based on these biases in the annual mean, Taylor diagram metrics, and RMSE, there is virtually no progress in the simulation fidelity of RSDS, RLUT, and RSUT fluxes from CMIP3 to CMIP5. A persistent systematic bias in CMIP3 and CMIP5 is the underestimation of RSUT and overestimation of RSDS and RLUT in the convectively active regions of the tropics. The amount of total ice and liquid atmospheric water content in these areas is also underestimated. We hypothesize that at least a part of these persistent biases stem from the common global climate model practice of ignoring the effects of precipitating and/or convective core ice and liquid in their radiation calculations.

Design of a Common Mode Filter by Using Planar Electromagnetic Bandgap Structures

In this paper, an embedded electromagnetic bandgap structure is proposed for harmonic filtering of differential signal's undesired common mode components. Rather than use lumped circuit components and likely causing some degradation to the intended high speed differential signal, the embedded planar common mode filter causes no degradation to the intended signal, and enhances the signal integrity and electromagnetic compatibility performance of the system. Single ended and differential traces are considered and the impact of the common mode filtering is measured in terms of mixed mode scattering parameters and eye diagram metrics. The systematic procedure to design such a structure is outlined, and its design robustness is also verified.

Phonon band structure and thermal transport correlation in a layered diatomic crystal

To elucidate the relationship between a crystal's structure, its thermal conductivity, and its phonon dispersion characteristics, an analysis is conducted on layered diatomic Lennard-Jones crystals with various mass ratios. Lattice dynamics theory and molecular dynamics simulations are used to predict the phonon dispersion curves and the thermal conductivity. The layered structure generates directionally dependent thermal conductivities lower than those predicted by density trends alone. The dispersion characteristics are quantified using a set of novel band diagram metrics, which are used to assess the contributions of acoustic phonons and optical phonons to the thermal conductivity. The thermal conductivity increases as the extent of the acoustic modes increases, and decreases as the extent of the stop bands increases. The sensitivity of the thermal conductivity to the band diagram metrics is highest at low temperatures, where there is less anharmonic scattering, indicating that dispersion plays a more prominent role in thermal transport in that regime. We propose that the dispersion metrics (i) provide an indirect measure of the relative contributions of dispersion and anharmonic scattering to the thermal transport, and (ii) uncouple the standard thermal conductivity structure-property relation to that of structure-dispersion and dispersion-property relations, providing opportunities for better understanding of the underlying physical mechanisms and a potential tool for material design.

A Survey of Metrics for UML Class Diagrams.

The demand for increased software quality has resulted in quality being more of differentiator between products than it ever has been before. For this reason, software developers need objective and valid measures for use in the evaluation and improvement of product quality from the initial stages of development. Class diagrams are a key artifact in the development of object-oriented (OO) software because they lay the foundation for all later design and implementation work. It follows that emphasizing class diagram quality may significantly contribute to higher quality OO software systems. The primary aim of this work, therefore, is to present a survey, as complete as possible, of the existing relevant works regarding class diagram metrics. Thus, from works previously published, researchers and practitioners alike may gain broad and ready access to insights for measuring these quality characteristics. Another aim of this work is to help reveal areas of research either lacking completion or yet to undertaken.