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The need of the customers to be connected to the network at all times has led to the evolution of mobile technology. Operating systems play a vitol role when we talk of technology. Nowadays, Android is one of the popularly used operating system in mobile phones. Authors have analysed three stable versions of Android, 6.0, 7.0 and 8.0. Incorporating a change in the version after it is released requires a lot of rework and thus huge amount of costs are incurred. In this paper, the aim is to reduce this rework by identifying certain parts of a version during early phase of development which need careful attention. Machine learning prediction models are developed to identify the parts which are more prone to changes. The accuracy of such models should be high as the developers heavily rely on them. The high dimensionality of the dataset may hamper the accuracy of the models. Thus, the authors explore four dimensionality reduction techniques, which are unexplored in the field of network and communication. The results concluded that the accuracy improves after reducing the features.

Recently, blockchains have found a place in all sorts of industries, ranging from healthcare to the government sector, finance, real estate and utilities. Blockchain has proved to be advantageous to the applications which previously required a central authority and a trusted intermediary, as now blockchain allows them to operate in a decentralised manner and helps them to achieve the same functionality much more efficiently. Transactions in blockchains are trustless, that is, the involved parties without knowing each other and without establishing any trust relations, sit in any corner of the world and such transactions thus occur without any trusted middlemen. For authorisation of these transactions, strong cryptographic principles, which form an essential part of the blockchain infrastructure, are used. Smart contracts basically add a layer of logic and computation to the already existing trust infrastructure of the blockchain, and thus allows for appropriate, heavily automated and distributed workflows, to function efficiently. In this chapter, we outline the art of using smart contracts and blockchain technology to implement a decentralized, secure, transparent yet anonymous digital voting system.

Class Imbalance Problem is the major issue in machine intelligence producing biased classifiers that work well for the majority class but have a relatively poor performance for the minority class. To ensure the development of accurate prediction models, it is essential to deal with the class imbalance problem. In this paper, the class imbalance problem is handled using focused undersampling techniques viz. Cluster Based, Tomek Link and Condensed Nearest Neighbours which equalize the number of instances of the two types of classes by undersampling the majority class based on some particular criteria. This is in contrast to random undersampling where the data samples are selected randomly from the majority class leading to underfitting and loss of some important datapoints. To fairly compare and evaluate the performance of focused undersampling approaches, prediction models are constructed using popular machine learning classifiers like K-Nearest Neighbor, Decision Tree and Naive Bayes. The results have shown that Decision Tree outperformed other machine learning techniques. Comparing and contrasting the undersampling approaches for Decision Tree concluded Condensed Nearest Neighbours to be best amongst others.

Background: Non-Functional Requirements (NFR) have a direct impact on the architecture of the system, thus it is essential to identify NFRs in the initial phases of software development. Aim: The work is based on extraction of relevant keywords from NFR descriptions by employing text mining steps and thereafter classifying these descriptions into one of the nine types of NFRs. Method: For each NFR type, keywords are extracted from a set of pre-categorized specifications using Information-Gain measure. Then models using 8 Machine Learning (ML) techniques are developed for classification of NFR descriptions. A set of 15 projects (containing 326 NFR descriptions) developed by MS students at DePaul University are used to evaluate the models. Results: The study analyzes the performance of ML models in terms of classification and misclassification rate to determine the best model for predicting each type NFR descriptions. The Naïve Bayes model has performed best in predicting “maintainability” and “availability” type of NFRs. Conclusion: The NFR descriptions should be analyzed and mapped into their corresponding NFR types during the initial phases. The authors conducted cost benefit analysis to appreciate the advantage of using the proposed models.

Feature selection is one of the most significant steps of pre-processing which deals with extracting the pertinent subset of features from the available feature set. This reduces the dimensionality of the dataset which is a key challenge in the field of data mining. In this chapter, three types of search methods are used for feature selection, viz. local search, evolutionary search and metaheuristic search. The goal of this chapter is to evaluate the search ability of metaheuristic methods as they overcome the drawbacks of local search and evolutionary search methods. For this, the authors have conducted an exhaustive comparison amongst the search methods where a collection of nine meta-heuristic search methods (Ant search, Bat search, Bee search, Cuckoo search, Elephant search, Firefly search, Flower search, Harmony search, Wolf search) are compared with three local search and four evolutionary search methods. These search methods are validated against two open source datasets belonging to different domains (allow to provide stronger and reliable conclusion), viz. Pima Indians Diabetes dataset and Hepatocellular carcinoma dataset found in UCI machine learning repository. After extracting the useful features using all the search methods, models are predicted using ensemble classifiers, Random Forest, Logitboost, Adaboost and Bagging. To the best of authors’ knowledge, there is no work in literature which has done such an exhaustive evaluation and comparison of search methods (total 16 search methods are used) and then constructed ensemble models to determine the effectiveness of each search method. The results proved that meta-heuristic search methods are more optimal for feature selection process and are better than local and evolutionary search methods.

With the evolution of tools used for image editing in today’s world, the images can be manipulated with ease. Resizing, cloning, cropping, etc., becomes easy and fast while on the other end, a great challenge in front of the world is determining whether an image has been tempered or not. A very popular manipulation method for tempering images is Copy-move forgery in which a part of an image is duplicated and pasted in some other location of the same image. Major research has been going on in detection of copy-move forgery. A detailed review and analytical discussion has been presented in this paper along with pros and cons of each of the detection techniques used for copy-move forgery detection.

Changes in software systems are inevitable. Identification of change-prone modules can help developers to focus efforts and resources on them. In this article, the authors conduct various intra-project and cross-project change predictions. The authors use distributional characteristics of dataset to generate rules which can be used for successful change prediction. The authors analyze the effectiveness of meta-heuristic decision trees in generating rules for successful cross-project change prediction. The employed meta-heuristic algorithms are hybrid decision tree genetic algorithms and oblique decision trees with evolutionary learning. The authors compare the performance of these meta-heuristic algorithms with C4.5 decision tree model. The authors observe that the accuracy of C4.5 decision tree is 73.33%, whereas the accuracy of the hybrid decision tree genetic algorithm and oblique decision tree are 75.00% and 75.56%, respectively. These values indicate that distributional characteristics are helpful in identifying suitable training set for cross-project change prediction.

CAPTCHA stands for Completely Automated Public Turing Test to Tell Computers and Humans Apart. CAPTCHAs are used as security mechanism in web applications to differentiate between real users and automated users, also known as bots. Text-based CAPTCHAs are the popularly used CAPTCHA schemes due to their simplicity and thus, they are still being used despite the proposal of several attack mechanisms. In this work, the authors have proposed a novel approach to solve CAPTCHA schemes. In this approach, the authors have used Depth First Search algorithm for the extraction of characters from CAPTCHAs and Convolutional Neural Network for recognizing these extracted characters. The proposed approach was validated on 3000+ CAPTCHA schemes and proved to be efficient by providing an average accuracy of more than 92.0% in detecting CAPTCHA schemes.

Identifying and examining the change-prone parts of the software is gaining wide importance in the field of software engineering. This would help software practitioners to cautiously assign the resources for testing and maintenance. Software metrics can be used for constructing various classification models which allow timely identification of change prone classes. There have been various machine learning classification models proposed in the literature. However, due to varying results across studies, more research needs to be done to increase the confidence in the results and provide a valuable conclusion. In this paper, we have used a number of data analysis techniques (14 machine learning techniques and a statistical technique) to construct change prediction models and performed statistical testing to compare the performance of these models. The application of a large number of techniques will allow for fair evaluation and will thus, increase the conclusion validity of the study. The results are validated on five releases of an open source, widely used operating system in mobile phone and tablet computers, ‘Android’. To make the results more generalizable, we have also conducted inter-release and cross-project predictions. The results conclude that the machine learning techniques are effective in predicting change prone classes and thus, should be widely used by researchers and practitioners to reduce maintenance effort and thus efficient and better development of software.

Software metrics are widely used to predict the classes in a software that are comparatively more change prone than others. Developers construct various prediction models which are used for the prediction of change prone classes. Thus, focussed attention can be laid on such classes leading to saving of lots of resources in terms of money, manpower and cost. But construction and usage of the prediction models using metrics is not always efficient and practical for developers. Thus, the alternative approach can be to identify certain alarming values of metrics above which a class is said to be at high risk. These alarming values are known as thresholds of the metrics. The main focus of this study is to identify metric threshold values using receiver operating characteristic curves. We have used threshold values to identify change prone classes (or classes that may lead to some risk in future) so that developers can carefully examine and pay attention on those classes. The study has used five release...

There are numerous reasons leading to change in software such as changing requirements, changing technology, increasing customer demands, fixing of defects etc. Thus, identifying and analyzing the change-prone classes of the software during software evolution is gaining wide importance in the field of software engineering. This would help software developers to judiciously allocate the resources used for testing and maintenance. Software metrics can be used for constructing various classification models which can be used for timely identification of change prone classes. Search based algorithms which form a subset of machine learning algorithms can be utilized for constructing prediction models to identify change prone classes of software. Search based algorithms use a fitness function to find the best optimal solution among all the possible solutions. In this work, we analyze the effectiveness of hybridized search based algorithms for change prediction. In other words, the aim of this work is to find whether search based algorithms are capable for accurate model construction to predict change prone classes. We have also constructed models using machine learning techniques and compared the performance of these models with the models constructed using Search Based Algorithms. The validation is carried out on two open source Apache projects, Rave and Commons Math. The results prove the effectiveness of hybridized search based algorithms in predicting change prone classes of software. Thus, they can be utilized by the software developers to produce an efficient and better developed software. Used hybridized search based algorithms to identify change prone classes.For empirical validation, two open source projects (Apache Rave,Commons Math) used.Assessed performance of search based algorithms using g-mean and accuracy.Machine learning models constructed and performance compared with hybridised models.Results showed that hybridised models outperformed machine learning models.

As a software system evolves, the changes are introduced at every stage of its development. The timely identification of change prone classes is very important to reduce the costs associated with the maintenance phase. Thus, the author has developed various models which can be used in the design phase (one of the early phases) to identify the parts of software which are more change prone than others. Software metrics along with the change data can be used for developing the models. In this study, the author has investigated the performance of various ensemble learners and a statistical model for identifying the classes which are change prone. The use of ensemble learners gives the researchers an opportunity to analyze and investigate them in the area of change prediction. The empirical validation is carried on five official releases of Android operating system. The overall results of the study indicate that the ensemble learners are capable of effective prediction.

Software change prediction is important to economically schedule allocation of resources during various phases of software maintenance and testing. Furthermore, exact characterization of progress inclined and non-change inclined classes is significant in beginning times of programming advancement life cycle since that helps with creating financially savvy quality programming for real-time use. A good prediction model should predict both the change and non-change prone classes with high accuracy. However, most practical datasets have underrepresented information and serious class appropriation skews. Due to imbalanced data, the minority classes are not predicted accurately causing poor planning of resources. Popular operating systems like Android get updated very fast. In the current scenario, it is essential to recognize change prone and non-change prone classes with precision in newer versions of such software that are updated very frequently. In this paper, we give a complete survey of various machine learning models to predict change prone classes algorithms using sampling technologies like resampling and spreadsubsampling on six open source datasets having imbalanced data. The experimental result of the study advocates that resampling technique consistently and significantly improves the performance of all the models.

For software development, availability of resources is limited, thereby necessitating efficient and effective utilization of resources. This can be achieved through prediction of key attributes, which affect software quality such as fault proneness, change proneness, effort, maintainability, etc. The primary aim of this chapter is to investigate the relationship between object-oriented metrics and change proneness. Predicting the classes that are prone to changes can help in maintenance and testing. Developers can focus on the classes that are more change prone by appropriately allocating resources. This will help in reducing costs associated with software maintenance activities. The authors have constructed models to predict change proneness using various machine-learning methods and one statistical method. They have evaluated and compared the performance of these methods. The proposed models are validated using open source software, Frinika, and the results are evaluated using Receiver Operating Characteristic (ROC) analysis. The study shows that machine-learning methods are more efficient than regression techniques. Among the machine-learning methods, boosting technique (i.e. Logitboost) outperformed all the other models. Thus, the authors conclude that the developed models can be used to predict the change proneness of classes, leading to improved software quality.

Software product quality can be enhanced significantly if we have a good knowledge and understanding of the potential faults therein. This paper describes a study to build predictive models to identify parts of the software that have high probability of occurrence of fault. We have considered the effect of thresholds of object-oriented metrics on fault proneness and built predictive models based on the threshold values of the metrics used. Prediction of fault prone classes in earlier phases of software development life cycle will help software developers in allocating the resources efficiently. In this paper, we have used a statistical model derived from logistic regression to calculate the threshold values of object oriented, Chidamber and Kemerer metrics. Thresholds help developers to alarm the classes that fall outside a specified risk level. In this way, using the threshold values, we can divide the classes into two levels of risk - low risk and high risk. We have shown threshold effects at various risk levels and validated the use of these thresholds on a public domain, proprietary dataset, KC1 obtained from NASA and two open source, Promise datasets, IVY and JEdit using various machine learning methods and data mining classifiers. Interproject validation has also been carried out on three different open source datasets, Ant and Tomcat and Sakura. This will provide practitioners and researchers with well formed theories and generalised results. The results concluded that the proposed threshold methodology works well for the projects of similar nature or having similar characteristics.

Classes in object-oriented software systems are continuously subject to change. Change prediction is a very important activity in software development. Change data consists of the number of lines of codes added, deleted and modified for each common class between any two versions of a software system. It is important to develop tools to calculate change data and object-oriented metrics that will assist software practitioners in identifying change prone classes in early stages of the software development life cycle. In this paper, we develop a tool, Change Report Generator (CRG) to generate the change report from software source codes of various versions of open-source software. We also extend this tool to automate object-oriented metrics calculation from the source codes of software systems. The generated files store the total number of changes class wise and corresponding values of different object-oriented metrics for each common class between the two versions. This paper gives an overview of some of the applications of the collected data like statistical comparison of two versions and prediction of change-prone classes.

Software change prediction deals with identifying the classes that are prone to changes during the early phases of software development life cycle. Prediction of change prone classes leads to higher quality, maintainable software with low cost. This study reports a systematic review of change prediction studies published in journals and conference proceedings. This review will help researchers and practitioners to examine the previous studies from different viewpoints: metrics, data analysis techniques, datasets, and experimental results perspectives. Besides this, the research questions formulated in the review allow us to identify gaps in the current technology. The key findings of the review are: (i) less use of method level metrics, machine learning methods and commercial datasets; (ii) inappropriate use of performance measures and statistical tests; (iii) lack of use of feature reduction techniques; (iv) lack of risk indicators used for identifying change prone classes and (v) inappropriate use of validation methods.

Due to various reasons such as ever increasing demands of the customer or change in the environment or detection of a bug, changes are incorporated in a software. This results in multiple versions or evolving nature of a software. Identification of parts of a software that are more prone to changes than others is one of the important activities. Identifying change prone classes will help developers to take focused and timely preventive actions on the classes of the software with similar characteristics in the future releases. In this paper, we have studied the relationship between various object oriented (OO) metrics and change proneness. We collected a set of OO metrics and change data of each class that appeared in two versions of an open source dataset, 'Java TreeView', i.e., version 1.1.6 and version 1.0.3. Besides this, we have also predicted various models that can be used to identify change prone classes, using machine learning and statistical techniques and then compared their performance. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the models predicted using both machine learning and statistical methods demonstrate good performance in terms of predicting change prone classes. Based on the results, it is reasonable to claim that quality models have a significant relevance with OO metrics and hence can be used by researchers for early prediction of change prone classes.