Advances and Challenges in Software Refactoring: A Tertiary Systematic Literature Review

Software refactoring is one of the most critical aspects of software maintenance. It improves the quality of the software, reduces potential occurrence of bugs and keeps the code easier to maintain, extend and read. The process of refactoring supports and enables the developers to improve the design of software without changing the behavior. However, the automation of this process is complex for developers and software engineers since it is subjective, time and resource consuming. In this context, many literature reviews have analyzed the existing effort made by researchers to facilitate refactoring, as a core software engineering practice. This paper, aims in integrating all the existing research outcomes by performing a tertiary study on all the secondary studies, done in the area of refactoring. Based on our analysis we notice that there are many area of software refactoring that are under studied. As an outcome of this review, several classifications of existing studies were provided to showcase all the studies targeting the automation of refactoring along with explaining what metrics and objectives were used as means to drive refactoring and how it was assessed. This thesis also aims in unveiling areas of future directions for the research community in order to consolidate their efforts in improving the refactoring as a practice

Detecting Anomalies in Object Appearance and Motion Dynamics

Machine common sense remains a broad, potentially unbounded problem in AI. Our focus is to move toward AI systems that can develop common-sense reasoning similar to humans to detect anomalies. In particular, we study the problem of detecting the violation of expectations when object appearance or motion dynamics change from simulated experiments. We have developed a system of multiple components to solve the problem of machine common sense. The system contains three main components: a two-stage tracker, a role assigner, and a rule-based reasoning agent. We evaluate our system on scenes from the DARPA Machine Common Sense, Passive Violation of Expectation task

Predictors of Culture Competence among Nursing Students in Riyadh City- Saudi Arabia

Cultural diversity is one of the challenges for nurses working in multicultural communities, as Kingdom of Saudi Arabia community. Aim of the study was to investigate the cultural competence of Saudi nursing students in Riyadh city. Design: Cross-sectional. Quantitative method applied with self-administered questionnaire. Sample: Convenience sample consists of 205 participants from five private and governmental colleges collected between November 2016 and January 2017. Results: Response rate 70%, the majority of the sample were females (58%), 50% of the participants didn’t get any training regarding care for cultural diverse patients. 36 % of the sample didn’t live in environment with different cultural people. However the majority (87%) provides a care for diverse cultural patients and deal with patients from different cultures. Total scores of culture competence mean among nursing students in Riyadh city was 74.54 ± 14.9. For each item the highest mean was 3.96 ± 0.96 which related to the ability to teach and guide other nurses to display appropriate behavior. Keywords: Nursing students, Cultural, Competence

Masader Plus: A New Interface for Exploring +500 Arabic NLP Datasets

Masader (Alyafeai et al., 2021) created a metadata structure to be used for cataloguing Arabic NLP datasets. However, developing an easy way to explore such a catalogue is a challenging task. In order to give the optimal experience for users and researchers exploring the catalogue, several design and user experience challenges must be resolved. Furthermore, user interactions with the website may provide an easy approach to improve the catalogue. In this paper, we introduce Masader Plus, a web interface for users to browse Masader. We demonstrate data exploration, filtration, and a simple API that allows users to examine datasets from the backend. Masader Plus can be explored using this link https://arbml.github.io/masader. A video recording explaining the interface can be found here https://www.youtube.com/watch?v=SEtdlSeqchk

Smad2 overexpression and the progression of periodontal disease

Periodontitis is a chronic inflammatory disease, characterized by destruction of the periodontal attachment apparatus including the alveolar bone. Previous studies have provided evidence for the involvement of transforming growth factor beta (TGF-β) signaling in periodontitis progression. TGF-β signaling is responsible for a variety of cellular processes including proliferation, differentiation and apoptosis. The SMAD2 transcription factor lies at the heart of TGF-β intracellular mediators. Previous authors have reported the effect of Smad2 overexpression on multiple mouse tissues (Ito et al 2001), but did not report the role of Smad2 overexpression on the progression of periodontal disease. We hypothesized that Smad2 overexpression alters apoptosis, cell proliferation, and inflammatory cytokine secretions in the junctional epithelium (JE), leading to periodontal attachment loss. A mouse model that overexpresses Smad2 in epithelial cells driven by the cytokeratin 14 promoter (K14) was used to test the hypotheses. The K14-Smad2 mice findings were compared to those observed in wild type (WT) mice that served as controls. The results of the study showed that Smad2 overexpression reduced the histological surface area of JE when compared to WT mice. The reduction of the JE surface area in K14-Smad2 mice was attributed to an increased apoptotic index and a reduced proliferation rate. The overexpression of Smad2 increased the apoptotic index by down regulating Bcl2, an antiapoptotic molecule. Smad2 overexpression also reduced the proliferation rate of the JE cells in K14-Smad2 mice by upregulating c-Myc, which in turn upregulates phosphorylated retinoblastoma P15, and P27. The overexpression of Smad2 resulted in severe alveolar bone loss in the K14-Smad2 mice when compared to the WT controls. Smad2 overexpression resulted in a reduction in the bone density and bone volume in the K14-Smad2 mice when compared to their WT counterparts. The severe alveolar bone loss in K14-Smad2 mice was attributed to an upregulation in tumor necrosis factor alpha (TNF-α) , RANKL and increased osteoclast numbers. In summary the overexpression of Smad2 reduced the histological surface of JE and resulted in severe bone loss that follows a chronic disease pattern in K14-Smad2 mice.Dentistry, Faculty ofGraduat

A New Exponential Distribution to Model Concrete Compressive Strength Data

Concrete mixtures can be developed to deliver a broad spectrum of mechanical and durability properties to satisfy the configuration conditions of construction. One technique for evaluating the compressive strength of concrete is to suppose that it pursues a probabilistic model from which it is reliability estimated. In this paper, a new technique to generate probability distributions is considered and a new three-parameter exponential distribution as a new member of the new family is presented in detail. The proposed distribution is able to model the compressive strength of high-performance concrete rather than some other competitive models. The new distribution delivers decreasing, increasing, upside-down bathtub and bathtub-shaped hazard rates. The maximum likelihood estimation approach is used to estimate model parameters as well as the reliability function. The approximate confidence intervals of these quantities are also obtained. To assess the performance of the point and interval estimations, a simulation study was conducted. We demonstrate the performance of the offered new distribution by investigating one high-performance concrete compressive strength dataset. The numerical outcomes showed that the maximum likelihood method provides consistent and asymptotically unbiased estimators. The estimates of the unknown parameters as well as the reliability function perform well as sample size increases in terms of minimum mean square error. The confidence interval of the reliability function has an appropriate length utilizing the delta method. Moreover, the real data analysis indicated that the new distribution is more suitable when compared to some well-known and some recently proposed distributions to evaluate the reliability of concrete mixtures

Inferences for Nadarajah–Haghighi Parameters via Type-II Adaptive Progressive Hybrid Censoring with Applications

This study aims to investigate the estimation problems when the parent distribution of the population under consideration is the Nadarajah–Haghighi distribution in the presence of an adaptive progressive Type-II hybrid censoring scheme. Two approaches are considered in this regard, namely, the maximum likelihood and Bayesian estimation methods. From the classical point of view, the maximum likelihood estimates of the unknown parameters, reliability, and hazard rate functions are obtained as well as the associated approximate confidence intervals. On the other hand, the Bayes estimates are obtained based on symmetric and asymmetric loss functions. The Bayes point estimates and the highest posterior density Bayes credible intervals are computed using the Monte Carlo Markov Chain technique. A comprehensive simulation study is implemented by proposing different scenarios for sample sizes and progressive censoring schemes. Moreover, two applications are considered by analyzing two real data sets. The outcomes of the numerical investigations show that the Bayes estimates using the general entropy loss function are preferred over the other methods

Estimation of Reliability Indices for Alpha Power Exponential Distribution Based on Progressively Censored Competing Risks Data

In reliability analysis and life testing studies, the experimenter is frequently interested in studying a specific risk factor in the presence of other factors. In this paper, the estimation of the unknown parameters, reliability and hazard functions of alpha power exponential distribution is considered based on progressively Type-II censored competing risks data. We assume that the latent cause of failures has independent alpha power exponential distributions with different scale and shape parameters. The maximum likelihood method is considered to estimate the model parameters as well as the reliability and hazard rate functions. The approximate and two parametric bootstrap confidence intervals of the different estimators are constructed. Moreover, the Bayesian estimation method of the unknown parameters, reliability and hazard rate functions are obtained based on the squared error loss function using independent gamma priors. To get the Bayesian estimates as well as the highest posterior credible intervals, the Markov Chain Monte Carlo procedure is implemented. A comprehensive simulation experiment is conducted to compare the performance of the proposed procedures. Finally, a real dataset for the relapse of multiple myeloma with transplant-related mortality is analyzed

Reliability and the engineering applications of the generalized half-normal model via an adaptive progressive hybrid censored mechanism

Recently, the generalized half-normal distribution with decreasing, increasing, and bathtub hazard function shapes was proposed, making it a more applicable, reliable, and flexible lifespan model. The task of estimating the unknown parameters and reliability features of the generalized half-normal distribution is looked at using adaptive progressively type-II hybrid censored data. The maximum likelihood and Bayesian estimation methods are both considered for this purpose. Two approximated confidence intervals, Bayes and highest posterior density intervals, are acquired for the various parameters. The Bayes estimates are obtained based on symmetric and asymmetric loss functions under the assumption of independent gamma priors. The Markov chain Monte Carlo approach is used to compute Bayes estimates as well as the various Bayes intervals. Monte Carlo experiments are used for assessing the efficiency of the various approaches. Finally, analysis is performed on two actual-life engineering datasets