Comparing Hybrid Tool for Static and Dynamic Object-Oriented Metrics

Summary form only given. Software metrics have become an integral part of software development and are used during every phase of the software development life cycle. Research in the area of software metrics tends to focus predominantly on static metrics that are obtained by static analysis of the software artifact. But software quality attributes such as performance and reliability depend on the dynamic behavior of the software artifact. Estimating software quality attributes based on dynamic metrics for the software system are more accurate and realistic. The research presented in this paper attempts to narrow the gap between static metrics and dynamic metrics, and lay the foundation for a more systematic approach to estimate the dynamic behavior of a software system early in the software development cycle. Focusing on coupling metrics, we present an empirical study to analyze the relationship between static and dynamic coupling metrics and propose the concept of pseudo dynamic metrics to estimate the dynamic behavior early in the software development lifecycle.

Software coupling is considered one of the most crucial issues in the software industry. The measurement of coupling gives a quantitative indication of internal features that are correlated to external quality attributes such as reliability, maintainability, extendibility, functionality and usability of the software. Various static and dynamic metrics have been proposed by several academicians and industry experts to evaluate software coupling. Although the values of static metrics can be easily extracted from the source code without its execution, researchers have emphasized the significance of dynamic metrics over static metrics in terms of program comprehensibility, change proneness, maintainability, etc. This chapter presents some of the dynamic coupling measures and their comparative analysis: (a) parametric-based analysis of six parameters, i.e. quality focus, measured entity, theoretical validation, dynamic analysis approach, environment, and statistical analysis technique; (b) functional-based analysis. Only those metrics which possess the required amount of information have been considered in order to make a meaningful comparison possible. Based on the comparison, the observed results reveal that empirical validation conducted for the evaluation of dynamic coupling metrics is not adequate to make them applicable to industries.

Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions like budget planning, cost estimation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments. Many design metrics have proposed in literature to measure various constructs of Object Oriented (OO) paradigm such as class, coupling, cohesion, inheritance, information hiding and polymorphism and use them further in determining the various aspects of software quality. However, the use of conventional static metrics have found to be inadequate for modern OO software due to the presence of run time polymorphism, templates class, template methods, dynamic binding and some code left unexecuted due to specific input conditions. This gap gave a cue to focus on the use of dynamic metrics instead of traditional static metrics to capture the software characteristics and further deploy them for maintainability predictions. As the dynamic metrics are more precise in capturing the execution behavior of the software system, in the current empirical investigation with the use of open source code, we validate and verify the superiority of dynamic metrics over static metrics. Four machine learning models are used for making the prediction model while training is performed simultaneously using static as well as dynamic metric suite. The results are analyzed using prevalent prediction accuracy measures which indicate that predictive capability of dynamic metrics is more concise than static metrics irrespective of any machine learning prediction model. Results of this would be helpful to practitioners as they can use the dynamic metrics in maintainability prediction in order to achieve precise planning of resource allocation.

Several important aspects of software product quality can be evaluated using dynamic metrics that effectively capture and reflect the software's true runtime behavior. While the extent of research in this field is still relatively limited, particularly when compared to research on static metrics, the field is growing, given the inherent advantages of dynamic metrics. The aim of this work is to systematically investigate the body of research on dynamic software metrics to identify issues associated with their selection, design and implementation. Mapping studies are being increasingly used in software engineering to characterize an emerging body of research and to identify gaps in the field under investigation. In this study we identified and evaluated 60 works based on a set of defined selection criteria. These studies were further classified and analyzed to identify their relativity to future dynamic metrics research. The classification was based on three different facets: research focus, research type and contribution type. We found a strong body of research related to dynamic coupling and cohesion metrics, with most works also addressing the abstract notion of software complexity. Specific opportunities for future work relate to a much broader range of quality dimensions.

Dynamic functional connectivity (FC) analyses have provided ample information on the disturbances of global functional brain organization in patients with schizophrenia. However, our understanding about the dynamics of local FC in never-treated first episode schizophrenia (FES) patients is still rudimentary. Dynamic Regional Phase Synchrony (DRePS), a newly developed dynamic local FC analysis method that could quantify the instantaneous phase synchronization in local spatial scale, overcomes the limitations of commonly used sliding-window methods. The current study performed a comprehensive examination on both the static and dynamic local FC alterations in FES patients (N = 74) from healthy controls (HCs, N = 41) with resting-state functional magnetic resonance imaging using DRePS, and compared the static local FC metrics derived from DRePS with those calculated from two commonly used regional homogeneity (ReHo) analysis methods that are defined based on Kendall's coefficient of concordance (KCC-ReHo) and frequency coherence (Cohe-ReHo). Symptom severities of FES patients were assessed with a set of clinical scales. Cognitive functions of FES patients and HCs were assessed with the MATRICS consensus cognitive battery. Group-level analysis revealed that compared with HCs, FES patients exhibited increased static local FC in right superior, middle temporal gyri, hippocampus, parahippocampal gyrus, putamen, and bilateral caudate nucleus. Nonetheless, the dynamic local FC metrics did not show any significant differences between the two groups. The associations between all local FC metrics and clinical characteristics manifested scores were explored using a relevance vector machine. Results showed that the Global Assessment of Functioning score highest in past year and the Brief Visuospatial Memory Test-Revised task score were statistically significantly predicted by a combination of all static and dynamic features. The diagnostic abilities of different local FC metrics and their combinations were compared by the classification performance of linear support vector machine classifiers. Results showed that the inclusion of zero crossing ratio of DRePS, one of the dynamic local FC metrics, alongside static local FC metrics improved the classification accuracy compared to using static metrics alone. These results enrich our understanding of the neurocognitive mechanisms underlying schizophrenia, and demonstrate the potential of developing diagnostic biomarker for schizophrenia based on DRePS.

The measurement is an important field in the software engineering. This field studies the assignment of a value to a software attribute using metrics. In fact, the use of metrics, which are the defined measurement methods and the measurement scales, in software engineering allows among other goals to control the quality of the software products, the quality of the software processes or the evolution of the software projects. Usually, we distinct two kinds of metrics: the static and the dynamic ones. The static metrics can be obtained from the source code of the software. On the other side, the dynamic metrics requires the execution of the software. Consequently, the static metrics are the most applied because of their relative simplicity compared to the dynamic metrics. However, we think that the dynamic metrics are more appropriate in some software categories. Multi-Agent Systems (MAS) is one of these categories because of the flexible nature of the agents. Consequently, we propose in this paper the using of the Aspect-Oriented Programming (AOP) to measure the dynamic metrics of multi-agent systems. In fact, the proposed approach provides several advantages like the simplicity, the reusability and the extensibility. In order to validate the proposed approach, we developed a tool that allows measuring of some metrics of DIMA-based applications.

A fundamental issue in speech research concerns whether distinctions in terms of place of articulation are more succesfully captured by local (static) or global (and/or dynamic) properties of the speech signal. While most studies of place of articulation have investigated stop consonants, it is uncertain whether these properties can successfully classify fricatives. In the present study, both static and dynamic metrics were used to investigate place of articulation in fricatives. Static metrics include spectral peak location, noise duration, and noise amplitude; dynamic metrics include relative amplitude, locus equations, and spectral moments. Twenty speakers produced the fricatives /f, v, θ, ð, s, z, ∫, ȝ/ followed by the vowels /i, e, æ, ɑ, o, u/. Preliminary results based on ANOVA and discriminant analysis suggest that all metrics could distinguish nonsibilant /f, v, θ, ð/ from sibilant /s, z, ∫, ȝ/. In addition, spectral moments separated /s, z/ from /∫, ȝ/ with about 95% accuracy and /f, v/ from /θ, ð/ with about 65% accuracy while locus equations only served to separate labiodental /f, v/ from all other fricatives. Accuracy of the metrics will also be compared to human perception data. [Work supported by NIH.]

Dynamic metrics capture the run time features of object-oriented languages, i.e., runtime polymorphism, dynamic binding, etc., that are not covered by static metrics. Therefore, in this paper, we derived a new approach to measuring the software reusability of a design pattern based on dynamic metrics. To achieve this, the authors proposed a model based on five parameters, i.e., polymorphism, inheritance, number of children, coupling, and complexity, to measure the reusability factor by using various soft computing techniques, i.e., Fuzzy, Neural Network, and Neuro-Fuzzy. Further, we also compared the proposed model with four existing machine learning algorithms. Lastly, we found that the proposed model using the neuro-fuzzy technique is trained well and predicts well with MAE (Mean absolute error) 0.003 and RMSE (Root mean square error) 0.009 based on dynamic metrics. Hence, it is concluded that dynamic metrics are a better predictor of the reusability factor than static metrics.

This paper reports on the feasibility of using eye tracking as a tool for students’ performance assessment in a medical serious game. We are particularly interested in analyzing the relationship between learners’ visual behaviour and their performance while solving medical cases. The objective of this study is twofold. First, we analyze how the students visually explore the learning environment across different areas of interest. Second, we examine whether static and dynamic eye tracking metrics can have an impact on students’ reasoning performance. Results revealed statistically significant associations between eye movement metrics and students’ outcomes. Particularly dynamic metrics better reflected students’ analytical reasoning abilities. Our findings have implications for the educational technology community seeking to gain a deeper understanding of the students’ learning experience.

Test flakiness is a phenomenon occurring when a test case is non-deterministic and exhibits both a passing and failing behavior when run against the same code. Over the last years, the problem has been closely investigated by researchers and practitioners, who all have shown its relevance in practice. The software engineering research community has been working toward defining approaches for detecting and addressing test flakiness. Despite being quite accurate, most of these approaches rely on expensive dynamic steps, e.g., the computation of code coverage information. Consequently, they might suffer from scalability issues that possibly preclude their practical use. This limitation has been recently targeted through machine learning solutions that could predict the flakiness of tests using various features, like source code vocabulary or a mixture of static and dynamic metrics computed on individual snapshots of the system. In this paper, we aim to perform a step forward and predict test flakiness only using static metrics. We propose a large-scale experiment on 70 Java projects coming from the iDFlakies and FlakeFlagger datasets. First, we statistically assess the differences between flaky and non-flaky tests in terms of 25 test and production code metrics and smells, analyzing both their individual and combined effects. Based on the results achieved, we experiment with a machine learning approach that predicts test flakiness solely based on static features, comparing it with two state-of-the-art approaches. The key results of the study show that the static approach has performance comparable to those of the baselines. In addition, we found that the characteristics of the production code might impact the performance of the flaky test prediction models.

Channelized turbidite systems are often grouped into complexes and exhibit various stacking patterns, which play a crucial role in controlling the connectivity between high-permeability and low-permeability sedimentary bodies. While some studies have analyzed the static connectivity of different stacking patterns, few have quantitatively evaluated the dynamic implications of these patterns on fluid flow circulation at large scale, and no method have started to compare the outcome of different modeling methods. This study addresses this gap by quantitatively investigating the impact of different classes of geostatistical modeling methods on both static and dynamic connectivity using multiple metrics. Using an original object-based simulation method, we have stochastically generated 3 different stacking pattern sets of 100 realizations each with similar facies proportions, that represent: (i) disorganized channels, (ii) independent channels conditioned to a vertical sand proportion map, and (iii) organized stacking that reproduce vertical and lateral migration of channels. Channel internal heterogeneities are neglected and homogeneous properties are attached to the three main facies: channels, inner levees and outer levees. To analyze the hydrodynamic responses, we set up a two-phase system of oil and water. Reservoir simulations are performed for all 300 realizations, providing production curves, saturation field and pressure field. Classical dynamic metrics (breakthrough time and recovery efficiency) are completed by original ones, designed to understand what governs the differences between the sets: at 30% of the Pore Volume Injected, saturation front shapes are extracted and we compute their surface area and sphericity to get objective comparison criteria. Results reveal significant differences in flow behavior across the three studied sets of stacking models: Disorganized stacking patterns, and, to a smaller extent, the conditioned disorganized stacking patterns, exhibit delayed water breakthrough times and more optimistic recovery than organized stacking patterns, which have, however, a higher static connectivity. This unexpected result seems to be related to gravity-driven fluid segregation in the considered reservoir production settings. Overall, these results quantitatively confirm that the ability of the channel simulation method to generate realistic stacking pattern is essential. In addition, the lack of a clear relationship between static and dynamic connectivity metrics suggests the need for further research to develop more effective forecasting techniques for reservoir behavior in turbiditic channel settings. • Object-based geostatistical modeling strategies of channelized reservoirs impact connectivity, even for comparable facies proportions. • Organised stacking patterns show earlier breakthrough times and lower recovery efficiency than traditional independent channel models. • Without internal heterogeneity, channel aggradation entails a large role to gravity-driven phase segregation. • The tested static connectivity metrics appear of limited value to forecast dynamic behavior for all modeling strategies.

This chapter presents the basics of standard 6T SRAM bitcell and simulation setups for measurement of bitcell read and write stability metrics. Different static and dynamic stability metrics are investigated. Static stability metrics includes conventional butterfly curves obtained from the voltage transfer characteristics, the N-curve based metrics and their simulation setup for read and write stability are also discussed. The static stability metrics for large scale dense cache SRAM measured from bitline, wordline and bitcell supply voltage are also presented in this chapter. Apart from static stability metrics commonly used in SRAM design and development, dynamic stability metrics are also presented along with their simulations setups. Detailed simulation results and illustrations for static and dynamic stability metrics are the main focus of this chapter.

If we ask about which is the main difference between modelling a system using a traditional model like the entity relationship model or an object oriented model, from our point of view the answer is that, in the first one, the processes are not located somewhere, and, in the second one, the processes (operations or methods) are encapsulated in classes. The choice of the right classes to home every operation is essential for the behaviour of the system. It is totally useless to design a well built system, according to a lot of statics metrics, if the system does not run well after. In other words, dynamic metrics allowing to evaluate the behaviour of a system when it runs are much more useful than any static metrics used to tell if the system is correctly built or not. According to this, we propose in this paper, a new approach to evaluate a priori the behaviour of a system, by taking into account the notion of event cost and the notion of time (which is obviously essential). The final goal of this approach is to deliver information on the way operations have to be placed in classes in order to get better performances when the system is running. However, the proposal of metrics is of no value if their practical use is not demonstrated, either by means of case studies taken from real projects or by controlled experiments. For this reason, an optimisation tool is being under construction in order to provide solutions to this problem

Evaluating the critical source area concept of phosphorus loss from soils to water-bodies in agricultural catchments

Software testing is known to be expensive, time consuming and challenging. Although previous research has investigated relationships between several software properties and software testability the focus has been on static software properties. In this work we present the results of an empirical investigation into the possible relationship between runtime properties (dynamic coupling and key classes) and class testability. We measure both properties using dynamic metrics and argue that data gathered using dynamic metrics are both broader and more precise than data gathered using static metrics. Based on statistical analysis, we find that dynamic coupling and key classes are significantly correlated with class testability. We therefore suggest that these properties could be used as useful indicators of class testability.