Smart Learning Tools for Enhancing Basic Education System

Interactive Learning (IL) has evolved the practice of digital technology and virtual communication, mostly by students. IL can be provided by using interactive tools such as Specific, Measurable, Achievable, Realistic, and Timely (SMART) board or white boards etc. Facial expressions are receiving attention due to its recognized relevance in learning. The efficiency of interactive tools can be effectively measured from software engineering perspectives using software quality metrics that are process and product metrics. In this paper, product quality metrics from customers perspective was used as the interactive tools has assessed by the users. The aim of this paper is to realize the impact of interactive tools in primary education and to measure the effectiveness of interactive tools through facial expressions of students. We found this study very successful as only 15% of students had negative response of using interactive tools in the form of angry, sad and disgust however 85% of students had positive response that they had enjoyed in the form of happy, surprise and neutral in primary education leveland in secondary education level there is a 15% improvement has been found in the results. It was concluded from this study that most of the students were satisfied with the interactive tools and their academic performance was highly improved as compared to traditional system of learning

Quality Metrics measurement for Hybrid Systems (Aspect Oriented Programming – Object Oriented Programming)

This paper explores a new framework for calculating hybrid system metrics using software quality metrics aspect-oriented and object-oriented programming. Software metrics for qualitative and quantitative measurement is a mix of static and dynamic software metrics. It is noticed from the literature survey that to date, most of the architecture considered only the evaluation focused on static metrics for aspect-oriented applications. In our work, we mainly discussed the collection of static parameters , long with AspectJ-specific dynamic software metrics.The structure may provide a new direction for research while predicting software attributes because earlier dynamic metrics were ignored when evaluating quality attributes such as maintainability, reliability, and understandability of Asepect Oriented software. Dynamic metrics based on the fundamentals of software engineering are equally crucial for software analysis as are static metrics. A similar concept is borrowed with the introduction of dynamic software metrics to implement aspect-riented software development.Currently, we only propose a structure and model using static and dynamic parameters to test the aspect-oriented method, but we still need to validate the proposed approach.

Analysis of Developers’ Network and Change Burst Metrics as Predictors of Software Faults

Introduction: Many software quality metrics that can be used as proxies of measuring software quality by predicting software faults have previously been proposed. However determining a superior predictor of software faults given a set of metrics is difficult since prediction performances of the proposed metrics have been evaluated in non–uniform experimental contexts. There is need for software metrics that can guarantee consistent superior fault prediction performances across different contexts. Such software metrics would enable software developers and users to establish software quality. Objectives: This research sought to determine a predictor for software faults that requires least effort to detect software faults and has least cost of misclassifying software components as faulty or not given developers’ network metrics and change burst metrics. Methods: Experimental data for this study was derived from Jmeter, Gedit, POI and Gimp open source software projects. Logistic regression was used to predict faultiness of a file while linear regression was used to predict number of faults per file. Results: Change burst metrics model exhibited the highest fault detection probabilities with least cost of mis-classification of components as compared to the developers’ network model. Conclusion: The study found that change burst metrics could effectively predict software faults.

PaaS Optimization of Apache Applications Using System Parameter Tuning of Big Data Platforms in Distributed Computing

Widely used data processing platforms use distributed systems to process huge data efficiently. The aim of this article is to optimize the platform services by tuning only the relevant, tunable, system parameters and to identify the relation between the software quality metrics. The system parameters of data platforms based on the service level agreements can be defined and customized. In the first stage, the most significant parameters are identified and shortlisted using various feature selection approaches. In the second stage, the iterative runs of applications are executed for tuning these shortlisted parameters to identify the optimal value and to understand the impact of individual input parameters on the system output parameter. The empirical results imply significant improvement in performance and with which it is possible to render the proposed work optimizing the services offered by these data platforms.