ABSTRACT Open‐source software is evolved through the active participation of users. In general, a user request for bug fixing, the addition of new features, and feature enhancements. Due to this, the software repositories are increasing day by day at an enormous rate. Additionally, user distinct requests add uncertainty and irregularity to the reported bug data. The performance of machine learning algorithms drastically gets influenced by the inappropriate handling of uncertainty and irregularity in the bug data. Researchers have used machine learning techniques for assigning priority to the bug without considering the uncertainty and irregularity in reported bug data. In order to capture the uncertainty and irregularity in the reported bug data, the summary entropy–based measure in combination with the severity and summary weight is considered in this study to predict the priority of bugs in the open‐source projects. Accordingly, the classifiers are build using these measures for different machine learning techniques, namely, k‐nearest neighbor (KNN), naïve Bayes (NB), J48, random forest (RF), condensed nearest neighbor (CNN), multinomial logistic regression (MLR), decision tree (DT), deep learning (DL), and neural network (NNet) for bug priority prediction This research aims to systematically analyze the summary entropy–based machine learning classifiers from three aspects: type of machine learning technique considered, estimation of various performance measures: Accuracy, Precision, Recall, and F‐measure and through existing model comparison. The experimental analysis is carried out using three open‐source projects, namely, Eclipse, Mozilla, and OpenOffice. Out of 145 cases (29 products X 5 priority levels), the J48, RF, DT, CNN, NNet, DL, MLR, and KNN techniques give the maximum F‐measure for 46, 35, 28, 11, 15, 4, 3, and 1 cases, respectively. The result shows that the proposed summary entropy–based approach using different machine learning techniques performs better than without entropy‐based approach and also entropy‐based approach improves the Accuracy and F‐measure as compared with the existing approaches. It can be concluded that the classifier build using summary entropy measure significantly improves the machine learning algorithms' performance with appropriate handling of uncertainty and irregularity. Moreover, the proposed summary entropy–based classifiers outperform the existing models available in the literature for predicting bug priority.

Big data is an emerging trend of information technology. Large amount of data are growing very fast these days. To handle this huge amount of data, we need a technology which can extract the complex representation of data. Several approaches exist nowadays; one of the latest paradigm is deep learning. To excerpt difficult representation automatically, deep learning algorithms are used widely for unsupervised data. To resolve an extremely complex problem we used deep learning algorithm which is inspired by field of Artificial intelligence, whose goal is match human brain's potential to perceive, evaluate, grasp and decision making. The real motivation of the deep learning algorithm is to handle complex problem effectively. The unsupervised data representation is directly extracted without the participation of human. The main goal of deep learning is to make a machine which is independent of human intervention. It generates some patterns and their relationship, the algorithms perform an effective task based on that data.

Dramatic developments in localization techniques have been achieved recently to overcome the difficulties of the Internet of Things (IoT) paradigm. The diversity of IoT in nearly all sectors, which requires the connection among objects, makes localization an important aspect in the field as it adds to the features of IoT. Precisely estimating the position of an object is one of the main purposes of localization to facilitate the exchange of information among the objects. The aim of this work is to explore the existing localization techniques and to evaluate existing encapsulating classification systems. For the literature survey, we have searched in all major databases. Relevant papers (amongst more than hundreds of the papers) are considered and evaluated to contribute to this work. In conclusion, the presented survey will assist people who are working in the field of localization in IoT and different related methods.

The number of issues fixed in the current release of the software is one of the factors which decides the next release of the software. The source code files get changed during fixing of these issues. The uncertainty arises due to these changes is quantified using entropy based measures. We developed a Non-Homogeneous Poisson Process model for Open Source Software to understand the fixing of issues across releases. Based on this model, optimal release-updating using entropy and maximizing the active user's satisfaction level subject to fixing of issues up to a desired level, is investigated as well. The proposed models have been validated on five products of the Apache open source project. The optimal release time estimated from the proposed model is close to the observed release time at different active user's satisfaction levels. The proposed decision model can assist management to appropriately determine the optimal release-update time. The proposed entropy based model for issues estimation shows improvement in performance for 21 releases out of total 23 releases, when compared with well-known traditional software reliability growth models, namely GO model[1]and S-shaped model[2]. The proposed model is also found statistically significant.

Mining software repositories (MSR) is an important area of research. An international workshop on MSR has been established under the umbrella of international conference on software engineering (ICSE) in year 2004. The quality papers received and presented in the workshop has led to initiate full-fledged conference which purely focuses on issues related to mining software engineering data since 2007. This paper is the result of reviewing all the papers published in the proceedings of the conferences on Mining Software Repositories (MSR) and in other related conference/journals. We have analyzed the papers that contained experimental analysis of software projects related to data mining in software engineering. We have identified the data sets, techniques and tools used/ developed/ proposed in these papers. More than half of the papers are involved in the task accomplished by building or using the data mining tools to mine the software engineering data. It is apparent from the results obtained by analyzing these papers that MSR authors process the raw data which in general publicly available. We categorizes different tools used in MSR on the basis of newly developed, traditional data mining tools, prototype developed and scripts. We have shown the type of mining task that has been performed by using these tools along with the datasets used in these studies.

In bug repositories, we receive a large number of bug reports on daily basis. Managing such a large repository is a challenging job. Priority of a bug tells that how important and urgent it is for us to fix. Priority of a bug can be classified into 5 levels from PI to P5 where PI is the highest and P5 is the lowest priority. Correct prioritization of bugs helps in bug fix scheduling/assignment and resource allocation. Failure of this will result in delay of resolving important bugs. This requires a bug prediction system which can predict the priority of a newly reported bug. Cross project validation is also an important concern in empirical software engineering where we train classifier on one project and test it for prediction on other projects. In the available literature, we found very few papers for bug priority prediction and none of them dealt with cross project validation. In this paper, we have evaluated the performance of different machine learning techniques namely Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN) and Neural Network (NNet) in predicting the priority of the newly coming reports on the basis of different performance measures. We performed cross project validation for 76 cases of five data sets of open office and eclipse projects. The accuracy of different machine learning techniques in predicting the priority of a reported bug within and across project is found above 70% except Naive Bayes technique.

Predicting software defects is one of the key areas of research in software engineering. Researchers have devised and implemented a plethora of defect/bug prediction approaches namely code churn, past bugs, refactoring, number of authors, file size and age, etc by measuring the performance in terms of accuracy and complexity. Different mathematical models have also been developed in the literature to monitor the bug occurrence and fixing process. These existing mathematical models named software reliability growth models are either calendar time or testing effort dependent. The occurrence of bugs in the software is mainly due to the continuous changes in the software code. The continuous changes in the software code make the code complex. The complexity of the code changes have already been quantified in terms of entropy as follows in Hassan [9]. In the available literature, few authors have proposed entropy based bug prediction using conventional simple linear regression (SLR) method. In this paper, we have proposed an entropy based bug prediction approach using support vector regression (SVR). We have compared the results of proposed models with the existing one in the literature and have found that the proposed models are good bug predictor as they have shown the significant improvement in their performance.