Harnessing deep learning algorithms to predict software refactoring

During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying thetargeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers’ skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring predictionat the class level. The effectiveness of the proposed model is evaluated usinga very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring predictio

أحكـام ديـون الميـت وحقــوق دائنيــه – دراسة فقهية قانونية مقارنة – The Provisions of Dead Person\u27s Debt and His Creditors\u27 Rights - A Comparative Jurisprudence Legal Study

ملخص إنّ أحكام ديون الميت وحقوق دائنيه من المسائل المهمة في أيامنا هذه؛ فموضوعات الحق والميراث والديون من الموضوعات التي أشغلت الحيز الأكبر من جهد الفقهاء والعلماء ورجال القضاء والقانون، حيث يُعد موضوع قضاء ديون الميت من الأهمية، وجعل ترتيبه في المرتبة الثانية من الحقوق الثابتة بالموت. وجاءت هذه الدراسة؛ لتبين مفهوم علمي الميراث والتركة وأهميتهما في الإسلام، ثم جاء بيان الحقوق المتعلقة بالتركة، وحق الورثة، وانتهاء شخصية المتوفى القانونية، وآلية حماية ديون أصحاب الحقوق، وآلية إثبات الديون على التركة، وحقوق الغرماء، ومدى استقلالية شخص الوارث عن شخصية المورث تجاه الدائن، وحماية ديون الغرماء. وقد توصلت الدراسة إلى مجموعة من النتائج منها أن كل وارث ملزم بتسديد نسبة الدين للدائن بنفس نسبة ما آل إليه من التركة، وأن الديون لها أثرها على قسمة التركة، وأن موت المدين له أثره على الدين المؤجل. كما توصي الدراسة بضرورة العمل على استحداث تشريعات خاصة بأحكام ديون الميت، مع ضرورة إيجاد مذكرة إيضاحية لهذه التشريعات وتفسير القرارات القضائية. The Provisions of Dead Person\u27s Debt and His Creditors\u27 Rights - A Comparative Jurisprudence Legal Study Abstract The provisions on the debt of the deceased and the rights of his creditors are important issues today. The issues of right, inheritance and debt are among the subjects that have taken up most of the efforts of jurists, scholars, and the law. The issue of paying off the debts of the dead is of importance and placed secondly among the rights that inviolable by death. This study showed the concept of inheritance, legacy, its importance, and the characteristics of this system in the Islam. Then came the statement of the rights related to the inheritance, the right of the heirs, the end of the legal personality of the deceased, the numerous of heirs’ rights, the mechanism for protecting the debts of rights holders, the mechanism for proving debts on the estate, the rights of debtors, the extent to which the heir is independent of the inherited towards the creditor and Protection of debtors\u27 debts. The study reached a set of results, including that each heir is obligated to pay the debt ratio to the creditor in the same proportion as the estate devolved to him, the debt had an impact on the division of legacies, and debtor\u27s death has an effect on deferred debt. The study also recommends the need for the development of particular legislations that should be introduced for debt provisions for deceased persons, and that an explanatory memorandum should be drawn upon such legislations besides the interpretation of judicial decisions

Towards Change Propagating Test Models In Autonomic and Adaptive Systems

The major motivation for self-adaptive computing systems is the self-adjustment of the software according to a changing environment. Adaptive computing systems can add, remove, and replace their own components in response to changes in the system itself and in the operating environment of a software system. Although these systems may provide a certain degree of confidence against new environments, their structural and behavioral changes should be validated after adaptation occurs at runtime. Testing dynamically adaptive systems is extremely challenging because both the structure and behavior of the system may change during its execution. After self adaptation occurs in autonomic software, new components may be integrated to the software system. When new components are incorporated, testing them becomes vital phase for ensuring that they will interact and behave as expected. When self adaptation is about removing existing components, a predefined test set may no longer be applicable due to changes in the program structure. Investigating techniques for dynamically updating regression tests after adaptation is therefore necessary to ensure such approaches can be applied in practice. We propose a model-driven approach that is based on change propagation for synchronizing a runtime test model for a software system with the model of its component structure after dynamic adaptation. A workflow and meta-model to support the approach was provided, referred to as Test Information Propagation (TIP). To demonstrate TIP, a prototype was developed that simulates a reductive and additive change to an autonomic, service-oriented healthcare application. To demonstrate the generalization of our TIP approach to be instantiated into the domain of up-to-date runtime testing for self-adaptive software systems, the TIP approach was applied to the self-adaptive JPacman 3.0 system. To measure the accuracy of the TIP engine, we consider and compare the work of a developer who manually identifyied changes that should be performed to update the test model after self-adaptation occurs in self-adaptive systems in our study. The experiments show how TIP is highly accurate for reductive change propagation across self-adaptive systems. Promising results have been achieved in simulating the additive changes as well

Adoption of smart watches as wearable technology in TESOL education among university students

Abstract This research explores the key motivating factors that influence student engagement with wearable technology (WT) in teaching English to speakers of other languages (TESOL) education. The study employs a novel, integrated framework that merges elements from the established technology acceptance model (TAM), Flow Theory, and additional factors pivotal to WT's efficacy, namely content richness and personal innovativeness. TAM and Flow Theory are utilized to identify the variables that drive WT adoption. Data for the research was gathered through an online survey comprising 23 questions, distributed among students in the KSA, with a total of 468 participants. Analysis of the data was conducted using Smart PLS Software to evaluate the research model, constructs, and hypotheses. The findings reveal varying contributions of the model's main constructs to WT acceptance. Specifically, content richness and innovativeness significantly enhance the perceived usefulness of WT. Additionally, perceived ease of use is a strong predictor of perceived usefulness and behavioral intention. The results underscore a growing demand for WT in TESOL Education, highlighting its role in streamlining information exchange among students. The study underscores the significance of certain external factors in technology acceptance, offering a fresh perspective by incorporating a framework that links TAM's perceived usefulness and ease of use with user satisfaction, content richness, and innovativeness. Moreover, the inclusion of Flow Theory adds a unique dimension by assessing engagement and control over WT. The research contributes to understanding the underlying motivations for employing WT in TESOL Education, primarily aimed at enhancing the effectiveness of both instructors and students. A limitation of this study is its focused application of TAM and Flow Theory within a specific educational context, which may not fully capture the complex societal, psychological, and gender-specific factors influencing WT adoption across diverse settings

Students' stress: The relationship of college students' stress variables to goal orientations, academic self-concept, and achievement variables.

Results revealed that Academic Self-Concept was the most influential and predictive of GPA. Cognitive Appraisal Strategies and Negative Personal Beliefs Stressors had strong influences on Academic Self-Concept. Furthermore, Negative Personal Beliefs Stressors strongly influenced Cognitive Appraisal Strategies. Additionally, Negative Personal Beliefs Stressors correlated strongly with all stressors and stress reactions. Finally, tests for "goodness of fit" indicated that the trimmed model fits the data well.Path analysis was utilized to test the validity of a hypothetical causal model, depicting the influence of students' stressors on Grade Point Average (GPA) when mediated by: (1) Cognitive Appraisal Strategies; (2) Cognitive Reactions; (3) Emotional Reactions; (4) Physiological Reactions; (5) Mastery Orientation; (6) Performance Orientation; and (7) Academic Self-Concept. Students' stressors consisted of: (1) Instruction and Evaluation; (2) Classroom Environment; (3) Teachers; (4) Work; (5) Family; and (6) Negative Personal Beliefs. The convenience sample consisted of college students (N = 582) from two major universities in the Midsouth: The University of Oklahoma, Norman, Oklahoma, and the University of Central Oklahoma, Edmond, Oklahoma. Students completed questionnaires designed to assess the aforementioned causal relationship

Vulnerability Assessments: A Case Study of Jordanian Universities

Websites of universities are considered the most important gateways to those Universities. They are heavily used by faculty members, employees, past, current and future students. They have a significant impact on University popularity and ranking. From a security perspective, those websites can be targets for a large number of possible security attacks such as: Flooding, denial of service (DoS), web defacement, etc. Attacks can be also from outsiders as well as insiders. In this paper, we conducted a vulnerability assessment on Websites of universities in Jordan. To ensure that our tests are constructive, we only employed passive penetration testing methods. Results showed that a significant number of those evaluated universities have critical or sever level vulnerabilities. Such vulnerabilities can be relatively easily be exploited by security attacks or attackers

Software Refactoring Prediction Using SVM and Optimization Algorithms

Test suite code coverage is often used as an indicator for test suite capability in detecting faults. However, earlier studies that have explored the correlation between code coverage and test suite effectiveness have not addressed this correlation evolutionally. Moreover, some of these works have only addressed small sized systems, or systems from the same domain, which makes the result generalization process unclear for other domain systems. Software refactoring promotes a positive consequence in terms of software maintainability and understandability. It aims to enhance the software quality by modifying the internal structure of systems without affecting their external behavior. However, identifying the refactoring needs and which level should be executed is still a big challenge to software developers. In this paper, the authors explore the effectiveness of employing a support vector machine along with two optimization algorithms to predict software refactoring at the class level. In particular, the SVM was trained in genetic and whale algorithms. A well-known dataset belonging to open-source software systems (i.e., ANTLR4, JUnit, MapDB, and McMMO) was used in this study. All experiments achieved a promising accuracy rate range of between 84% for the SVM–Junit system and 93% for McMMO − GA + Whale + SVM. It was clear that added value was gained from merging the SVM with two optimization algorithms. All experiments achieved a promising F-measure range between the SVM–Antlr4 system’s result of 86% and that of the McMMO − GA + Whale + SVM system at 96%. Moreover, the results of the proposed approach were compared with the results from four well known ML algorithms (NB-Naïve, IBK-Instance, RT-Random Tree, and RF-Random Forest). The results from the proposed approach outperformed the prediction performances of the studied MLs

ADBT Frame Work as a Testing Technique: An Improvement in Comparison with Traditional Model Based Testing

Software testing is an embedded activity in all software development life cycle phases. Due to the difficulties and high costs of software testing, many testing techniques have been developed with the common goal of testing software in the most optimal and cost-effective manner. Model-based testing (MBT) is used to direct testing activities such as test verification and selection. MBT is employed to encapsulate and understand the behavior of the system under test, which supports and helps software engineers to validate the system with various likely actions. The widespread usage of models has influenced the usage of MBT in the testing process, especially with UML. In this research, we proposed an improved model based testing strategy, which involves and uses four different diagrams in the testing process. This paper also discusses and explains the activities in the proposed model with the finite state model (FSM). The comparisons have been done with traditional model based testings in terms of test case generation and result

Repurposing SGLT2 Inhibitors for Neurological Disorders: A Focus on the Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a substantially increasing incidence rate. It is characterized by repetitive behavior, learning difficulties, deficits in social communication, and interactions. Numerous medications, dietary supplements, and behavioral treatments have been recommended for the management of this condition, however, there is no cure yet. Recent studies have examined the therapeutic potential of the sodium-glucose cotransporter 2 (SGLT2) inhibitors in neurodevelopmental diseases, based on their proved anti-inflammatory effects, such as downregulating the expression of several proteins, including the transforming growth factor beta (TGF-β), interleukin-6 (IL-6), C-reactive protein (CRP), nuclear factor κB (NF-κB), tumor necrosis factor alpha (TNF-α), and the monocyte chemoattractant protein (MCP-1). Furthermore, numerous previous studies revealed the potential of the SGLT2 inhibitors to provide antioxidant effects, due to their ability to reduce the generation of free radicals and upregulating the antioxidant systems, such as glutathione (GSH) and superoxide dismutase (SOD), while crossing the blood brain barrier (BBB). These properties have led to significant improvements in the neurologic outcomes of multiple experimental disease models, including cerebral oxidative stress in diabetes mellitus and ischemic stroke, Alzheimer’s disease (AD), Parkinson’s disease (PD), and epilepsy. Such diseases have mutual biomarkers with ASD, which potentially could be a link to fill the gap of the literature studying the potential of repurposing the SGLT2 inhibitors’ use in ameliorating the symptoms of ASD. This review will look at the impact of the SGLT2 inhibitors on neurodevelopmental disorders on the various models, including humans, rats, and mice, with a focus on the SGLT2 inhibitor canagliflozin. Furthermore, this review will discuss how SGLT2 inhibitors regulate the ASD biomarkers, based on the clinical evidence supporting their functions as antioxidant and anti-inflammatory agents capable of crossing the blood-brain barrier (BBB)