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Online news blogs and websites are becoming influential to any society as they accumulate the world in one place. Aside from that, online news blogs and websites have efficient strategies in grabbing readers’ attention by the headlines, that being so to recognize the sentiment orientation or polarity of the news headlines for avoiding misinterpretation against any fact. In this study, we have examined 3383 news headlines created by five different global newspapers. In the interest of distinguishing the sentiment polarity (or sentiment orientation) of news headlines, we have trained our model by seven machine learning and two deep learning algorithms. Finally, their performance was compared. Among them, Bernoulli naïve Bayes and Convolutional Neural Network (CNN) achieved higher accuracy than other machine learning and deep learning algorithms, respectively. Such a study will help the audience in determining their impression against or for any leader or governance; and will provide assistance to recognize the most indifferent newspaper or news blogs.

Software process metrics are being used for improving agile processes in software companies. This helps a lot in managing customer satisfaction and the growth of the company. Different studies have stated different process metrics and practitioners are using various metrics in terms of software process improvement. However, it is difficult to find out the best-suited metrics for every company as per the project nature. A systematic mapping study is used to identify potential process improvement metrics and finding the best metrics in agile development. We have found only 13 papers on process metrics. Finally, we have classified them into three categories assuming their impacting factors. This study provides an overview of agile software development process metrics that will help the practitioners to adopt efficient process metrics in Global Software Development (GSD).

With the ever-expanding of mobile technologies, maintaining software quality becomes a challenging job as a high volume of analyzes and high arrangements of features ought to be tested. Today, organizations are investing an expanding measure of energy and assets in guaranteeing the application is completely tried for the best client experience and ideal execution by the application. Automation in testing could be a great solution in this regard. Though there are mere tools for testing iOS applications, an open source mobile testing tool Appium is one of those. The purpose of this study is to discuss the detailed configuration of Appium for testing iOS applications and to address one of the major limitations of testing iOS applications using Appium, that is to test in multiple iOS devices using one Mac machine. This will support the iOS mobile industry to improve the quality of user experience by guiding the step by step set up of Appium for testing in commercial level and making it more cost effective.

In the field of information security, phishing URLs detection and prevention has recently become egregious. For detecting, phishing attacks several anti-phishing systems have already been proposed by researchers. The performance of those systems can be affected due to the lack of proper selection of machine learning classifiers along with the types of feature sets. A details investigation on machine learning classifiers (KNN, DT, SVM, RF, ERT and GBT) along with three publicly available datasets with multidimensional feature sets have been presented on this paper. The performance of the classifiers has been evaluated by confusion matrix, precision, recall, F1-score, accuracy and misclassification rate. The best output obtained from Random Forest and Extremely Randomized Tree with dataset one and three (binary class feature set) of 97% and 98% accuracy accordingly. In multiclass feature set (dataset two), Gradient Boosting Tree provides highest performance with 92% accuracy.

Stock price forecasting, is one of the most significant financial complexities, since data are not reliable and noisy, impacting many factors. This article offers a machine learning model for the stock price prediction using Support Vector Machine-Regression (SVR) with two different kernels which are Radial Basis Function (RBF) and linear kernel. This study shows the Prediction and accuracy comparison between Support Vector Regression (SVR) and Linear Regression (LR) and also the accuracy comparison for different kernels of Support vector Regression (SVR). The model has used sum squared error (SSE) to determine the accuracy of each algorithm; which has shown significant improvement than the other studies. This analysis is conducted on the price data of about five years of Grameenphone listed on Dhaka Stock Exchange (DSE). The highest accuracy was found with Linear Regression model in every case with the highest accuracy of about 97.07% followed by SVR (Linear) model and SVR (radial basis function) model with the highest accuracy rate of about 97.06% and 96.82%. In some cases the accuracy of SVR (radial basis function) was higher than SVR (linear). But it was the Linear Regression which had the highest accuracy of all in every case.

In global software development, requirements re-usability is a common practice which ultimately helps to maintain project quality and reduce both development time and cost. However, when a large-scale project is distributed, there are some critical factors needed to be maintained and managed for reusing requirements and it is considered a challenging job to interrelate the requirements between two identical projects. In this study, we have pointed out 48 challenges faced and 43 mitigation techniques used when implementing requirements re-usability in global software development projects among distributed teams. The challenges distributed teams frequently encounter can be divided into three considering issues as Communication, Coordination and Control of distributed teams in global software development. The results from this study can be used to plan development strategies while reusing requirements in distributed manners.

Code reviewing becomes a more popular technique to find out early defects in source code. Nowadays practitioners are going for peer reviewing their codes by their co-developers to make the source code clean. Working on a distributed or dispersed team, code review is mandatory to check the patches to merge. Code reviewing can also be a form of validating functional and non-functional requirements. Sometimes reviewers do not put structured comments, which becomes a bottle neck to developers for solving the findings or suggestions commented by the reviewers. For making the code review participation more effective, structured and efficient review comments is mandatory. Mining the repositories of five commercialized projects, we have extracted 15223 review comments and labelled them. We have used 8 different machine learning and deep learning classifiers to train our model. Among those Stochastic Gradient Descent (SGD) technique achieves higher accuracy of 80.32%. This study will help the practitioners to build up structured and effective code review culture among global software developers.

Cloud computing provides data storage and computing power based on user demand by assigning tasks to virtual resources. To deliver overall improved performance and meet challenges such as availability, resource utilization and reliability in the cloud, appropriate resource scheduling methods are needed. A number of metaheuristic optimization algorithms are used to solve the problem of resource scheduling. This work lists challenges and analyzes previous scheduling methods based on Genetic Algorithm (GA). It classifies the GA-based scheduling methods with respect to many parameters. At last, it presents the scopes of enhancement for future researchers.

Requirements re-usability in a distributed software development project is applied to increase system productivity, reliability, quality, decreasing system development sprint and maintaining consistency between two identical systems, which later help to reduce both project time and cost. Nowadays, most of the projects are driven by market so that the intention of this research is to identify the challenges faced by practitioners in requirements re-usability in distributed large-scale agile projects and to find out how practitioners apply the concept of re-usability to mitigating those challenges in distributed large-scale agile software development from requirement engineering or re-usability perspective. In this study, a survey is used to identify requirement re-usability challenges and mitigation approach from practitioners. From a series of semi-structured interview, we have identified 14 challenges and 10 mitigation approaches into three categories, such as communication, coordination, and control from the global software engineering perspective. The findings from this research will help industry people to make a decision in their industry oriented activities.

Topics generated by topic models are typically reproduced as a list of words. To decrease the cognitional overhead of understanding these topics for end-users, we have proposed labeling topics with a noun phrase that summarizes its theme or idea. Using the WordNet lexical database as candidate labels, we estimate natural labeling for documents with words to select the most relevant labels for topics. Compared to WUP similarity topic labeling system, our methodology is simpler, more effective, and obtains better topic labels.

An increasing number of people are changing their way of thinking by reading news headlines. The interactivity and sincerity present in online news headlines are becoming influential to society. Apart from that, news websites build efficient policies to catch people’s awareness and attract their clicks. In that case, it is a must to identify the sentiment polarity of the news headlines for avoiding misconception. In this paper, we analyze 3383 news headlines generated by five major global newspapers during a minimum of four consecutive months. In order to identify the sentiment polarity (or sentiment orientation) of news headlines, we use 7 machine learning algorithms and compare those results to find the better ones. Among those Bernoulli Naive Bayes technique achieves higher accuracy than others. This study will help the public to make any decision based on news headlines by avoiding misconception against any leader or governance and will help to identify the most neutral newspaper or news blogs.

The most widely recognized relative directions are left, right, forward and backward. This paper has presented a computational technique for tracking location by learning relative directions between two intelligent agents, where two agents communicate with each other by radio signal and one intelligent agent helps another intelligent agent to find location. This proposed method represents an alternative approach to GSM (Global System for Mobile Communications) for the AI (Artificial Intelligence), where no network may not be available. Our research paper has proposed Relative Direction Based Location Tracking (RDBLT) model for understanding how one intelligent agent assists another intelligent agent to find out the location by learning and identifying relative directions. Moreover, three proficient algorithms have been developed for constructing our proposed model.