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Social media has become a significant platform not only for communication, but also for business and marketing of content and services. These activities involve large volumes of data with different patterns that have to be harnessed for businesses or users to maximize their meaning and make relevant brand decisions. Machine learning algorithms and tools like Natural language Processing (NLP) facilitate the system’s ability to recognize, extract cloud data patterns, and classify them in a more realistic way. The previous method only made mere literal guesses to aid automated data tracking and decision-making. The aim of this study was to carry out social media data analytics using machine learning tools. In this study, we proposed novel Naïve Bayes and Maximum Entropy classifiers. These classifiers were used to conduct a sentiment analysis to categorize and analyze messages collected from Twitter in order to determine whether they are positive, negative or non-partisan, and ultimately get the targeted output. The experimental results were successful and the machine learning tools were accurate in determining the nature of the Twitter messages. The study demonstrates the capacity of machine learning algorithms in conducting actionable social media data mining and analytics.

Analogy-Based Estimation (ABE) is one of the promising estimation models used for predicting the software development effort. Researchers proposed different variants of the ABE model, but still, the most suitable procedure could not be produced for accurate estimation. In this study, an artificial Bee colony guided Analogy-Based Estimation (BABE) model is proposed which ensembles Artificial Bee Colony (ABC) with ABE for accurate estimation. ABC produces different weights, out of which the most appropriate is infused in the similarity function of ABE during the stage of model training, which are later used in the testing stage for evaluation. There are six real datasets utilized for simulating the model procedure. Five of these datasets are taken from the PROMISE repository. The predictive performance is improved for BABE over the existing ones. The most significant of its performance is found on the International Software Benchmarking Standards Group (ISBSG) dataset.

Nonfunctional requirements get less attention because functional requirements are considered more important in the domain of agile software methodologies. This is due to the lack of mature requirement elicitation methodologies and the nature of the software agile software development process. The less attention caused few solutions in the domain which lead to software project failure. Cloud computing helps to practice twelve (12) agile principles including nonfunctional requirement elicitation. This study proposed a semi-automated methodology which will help analyst and developers in eliciting nonfunctional requirements in agile development and cloud computing environment. The methodology used an NLP based automatic NFR extraction approach to fast the NFR elicitation process. The methodology is evaluated by applying on eProcurement dataset. The results are improved by 8.77% and 1.76% in terms of “Successful” NFR. It is decreased by 7.02% and 1.75% in term of “Partial success”, and 1.76% to 0.0% in term of “Failure” as compared to existing studies.

Combinatorial searching-based software testing (CSST) is a challenging optimization procedure. The achievement of optimal solutions involves a careful formulation of the optimization problem and the selection of an appropriate approach. Meta-heuristic searching procedures have proven to be effective for solving CSST issues. Black hole (BH) optimization is among the more recently developed meta-heuristic searching algorithms. While this approach has been observed to be an effective alternative to particle swarm optimization, its operation is based on only one swarm. To date, no efforts have been made to modify this approach to accommodate multiple swarms. This study proposes a new variant of BH that involves a combination of multiple swarms. The BH optimizer is modified from continuous searching to binary searching and subsequently applied for solving CSST. The evaluation is based on a modified-benchmarking mathematical function and well-known CSST problems. This modified BH method is superior to the original BH and the established particle swarm optimization (PSO) approach. In terms of CSST problems, binary multiple black hole (BMBH) optimizations generate reduction rates between 50% and more than 60% for t = 4 according to the problem.

During the recent years World Wide Web very fast increased a fundamental part in our everyday life. In commerce, personal relationship, the effect of the universal network has wholly changed the way people interact with each other and with machines. The problem is after rising the Artificial Intelligence to presenting human feelings, everything changed including web applications. In this paper, we describe the intelligent web applications as present and future of web applications, moreover we highlight the special features and their roles in increasing intelligence of web applications as well as impact this application in the process development web systems. The goals of this paper led to developers to create smart and modern web applications.

As the battery-operated power source of wireless sensor networks, energy consumption is a major concern. The medium-access protocol design solves the energy usage of sensor nodes while transmitting and receiving data, thereby improving the sensor network’s lifetime. The suggested work employs a hybrid algorithm to improve the energy efficiency of sensor networks with nodes that are regularly placed. Every node in this protocol has three operating modes, which are sleep mode, receive mode, and send mode. Every node enters a periodic sleep state in order to conserve energy, and after waking up, it waits for communication. During the sleep mode, the nodes turn off their radios in order to reduce the amount of energy they consume while not in use. As an added feature, this article offers a channel access mechanism in which the sensors can send data based on the Logical Link Decision (LLD) algorithm and receive data based on the adaptive reception method. It is meant to select acceptable intermediary nodes in order to identify the path from the source to the destination and to minimize data transmission delays among the nodes in the network scenario. Aside from that, both simulation and analytical findings are used to examine the activity of the suggested MAC, and the created models are evaluated depending on their performance. With regard to energy consumption, latency, throughput, and power efficiency, the result demonstrates that the suggested MAC protocol outperforms the corresponding set of rules. The extensive simulation and analytical analysis showed that the energy consumption of the proposed LELLMAC protocol is reduced by 22% and 76.9% the end-to-end latency is 84.7% and 87.4% percent shorter, and the throughput is 60.3% and 70.5% higher than the existing techniques when the number of node is 10 and 100 respectively.

Functional and non-functional requirements are important equally in software development. Usually, the requirements are expressed in natural languages. The functional and non-functional requirements are written inter-mixed in software requirement document. The extraction of requirement from the software requirement document is a challenging task. Most of the recent studies adopted a supervised learning approach for the extraction of non-functional requirements. However, there is a drawback of supervised learning such as training of model and retrain if the domain changed. The proposed approach manipulates the textual semantic of functional requirements to identify the non-functional requirements. The semantic similarity is calculated based on co-occurrence of patterns in large human knowledge repositories of Wikipedia. This study finds the similarity distance between the popular indicator keywords and requirement statements to identify the type of non-functional requirement. The proposed approach is applied to PROMISE “NFR dataset.” The performance of the proposed approach is measured in terms of precision, recall and F-measure. Furthermore, the research applies three pre-processing approaches (traditional, part of speech tagging and word augmentation) to increase the performance of NFR extraction. The proposed approach outperforms the results of existing studies.

Agile methods are based on frequent delivery of software, improved customer satisfaction, closed interaction with the clients and accommodation of requirement change at any stage of development. However, several challenges exist such as scalability, transparency, face-to-face communication, smooth control of development, and ability to build applications from distributed locations. A framework proposed for Agile Development using Cloud Computing (ADCC) in the earlier study is evaluated in the current study. The Malaysia Research and Education Network cloud is utilized to implement the framework. An industrial survey is conducted to evaluate the framework. The results of the industrial survey show that the ADCC framework has a positive impact on the performance of agile methods and overcomes some challenges found in distributed agile development. Furthermore, the survey verifies the challenges in agile development and the solutions covers these challenges so far.

To achieve the goal of creating products for a specific market segment, implementation of Software Product Line (SPL) is required to fulfill specific needs of customers by managing a set of common features and exploiting the variabilities between the products. Testing product-by-product is not feasible in SPL due to the combinatorial explosion of product number, thus, Test Case Prioritization (TCP) is needed to select a few test cases which could yield high number of faults. Among the most promising TCP techniques is similarity-based TCP technique which consists of similarity distance measure and prioritization algorithm. The goal of this paper is to propose an enhanced string distance and prioritization algorithm which could reorder the test cases resulting to higher rate of fault detection. Comparative study has been done between different string distance measures and prioritization algorithms to select the best techniques for similarity-based test case prioritization. Identified enhancements have been implemented to both techniques for a better adoption of prioritizing SPL test cases. Experiment has been done in order to identify the effectiveness of enhancements done for combination of both techniques. Result shows the effectiveness of the combination where it achieved highest average fault detection rate, attained fastest execution time for highest number of test cases and accomplished 41.25% average rate of fault detection. The result proves that the combination of both techniques improve SPL testing effectiveness compared to other existing techniques.

The accuracy of effort estimation in one of the major factors in the success or failure of software projects. Analogy-Based Estimation (ABE) is a widely accepted estimation model since its flow human nature in selecting analogies similar in nature to the target project. The accuracy of prediction in ABE model in strongly associated with the quality of the dataset since it depends on previous completed projects for estimation. Missing Data (MD) is one of major challenges in software engineering datasets. Several missing data imputation techniques have been investigated by researchers in ABE model. Identification of the most similar donor values from the completed software projects dataset for imputation is a challenging issue in existing missing data techniques adopted for ABE model. In this study, Fuzzy C-Mean Imputation (FCMI), Mean Imputation (MI) and K-Nearest Neighbor Imputation (KNNI) are investigated to impute missing values in Desharnais dataset under different missing data percentages (Desh-Miss1, Desh-Miss2) for ABE model. FCMI-ABE technique is proposed in this study. Evaluation comparison among MI, KNNI, and (ABE-FCMI) is conducted for ABE model to identify the suitable MD imputation method. The results suggest that the use of (ABE-FCMI), rather than MI and KNNI, imputes more reliable values to incomplete software projects in the missing datasets. It was also found that the proposed imputation method significantly improves software development effort prediction of ABE model.

Background: The word “Global Software Development” can be described as the development of software, with development teams spread across different geographical locations. Problem statement: The issues arise when there are gaps in information, workflows or processes, policies and others in the world. Objective: This paper aims to build an understanding of the risk in Global Software Development. Then, to identify category risk in Global Software Development and how Agile can reduce or mitigate their challenges. Method: This review paper using the standard systematic literature review method by Kitchehamm by reviewing and analyzing the relevant state-of -art techniques and approaches in the journal libraries based on the research questions. Results: The findings show that communication in Agile Global Software development is the main risk challenge. Contribution: The contributions of this paper may support the academician to propose and validate an enhanced approach to solve the issues if risk management in Agile GSD environment and assist the practitioners in choosing the most suitable and relevant method based on the requirement of Agile GSD project. This review paper is also focusing on the contribution to the Software Engineering Management and Software Engineering Models and Methods Knowledge Area.

Requirements engineering (RE) is an important phase of software engineering. During this phase, an important set of activities are carried out to manage requirements elicitation, verification, prioritization and validation. Dimension and dynamics of software development are changing with the passage of time. Economic growth in different sectors is increasing the demand for software development. This enhancement has introduced the concept of Value Base Software (VBS) development. Requirements prioritization is playing a vital role in ordering requirements to support the release planning of the software. A prioritization process is considered as highly complex process and depends on the nature and size of requirements. VBS systems are entirely different from typical software development, and prioritization process for VBS is also very challenging. A need arises from the provision of prioritization techniques to support the technical and business aspects-based prioritization. Existing techniques are not qualified to meet the expectation of the industry for VBS development. Therefore, this research contribution is an effort made, based on an intelligent decision support system for requirements prioritization in the domain of VBS system. Aspects based requirements prioritization is applied to many requirements and results are produced in two clusters. Results are claimed as a prioritized list of requirements for traditional as well as value-based system.

During the revolution of webs, several modern web applications appeared. After developing electronic devices, Ubiquitous web applications (UWA) becomes an important part of web applications. UWAs are a new type of web applications which are accessed in various contexts, meaning through different devices, by users with various interests, at any time from any place around the globe. The problem is UWA features made challenge during the process development of web engineering methods. Interaction Flow Modeling Language (IFML) is the recent modeling language has many concepts for developing interactions in web applications compared with other web engineering methods. In this paper, we present the role of IFML method to develop UWA features through analyzing IFML and designing a case study. The result of this paper becomes a guide for developers and increase the usability of IFML.

Modeling approaches based on standards are of paramount importance in the field of front-end design for web and mobile applications. Problems are often encountered during the selection of tools for designing applications for developers and researchers, particularly applications that are related to time and cost in the market and academia. The Interaction Flow Modeling Language (IFML) is a recently standardized modeling language designed for managing the content expression, user interaction and behavior control of front-end applications. IFML brings several benefits to the development process of web and mobile front-end applications. Thus, several tools have been developed for the exploitation of technical artifacts offered by current specifications. In this study, we discuss the tools proposed for modeling IFML to provide a comparative analysis while considering various criteria. The result can be used as a basis of tool selection for specific aspects.

Context Software quality can be assured by going through software testing process. However, software testing phase is an expensive process as it consumes a longer time. By scheduling test cases execution order through a prioritization approach, software testing efficiency can be improved especially during regression testing. Objective It is a notable step to be taken in constructing important software testing environment so that a system's commercial value can increase. The main idea of this review is to examine and classify the current test case prioritization approaches based on the articulated research questions. Method Set of search keywords with appropriate repositories were utilized to extract most important studies that fulfill all the criteria defined and classified under journal, conference paper, symposiums and workshops categories. 69 primary studies were nominated from the review strategy. Results There were 40 journal articles, 21 conference papers, three workshop articles, and five symposium articles collected from the primary studies. As for the result, it can be said that TCP approaches are still broadly open for improvements. Each approach in TCP has specified potential values, advantages, and limitation. Additionally, we found that variations in the starting point of TCP process among the approaches provide a different timeline and benefit to project manager to choose which approaches suite with the project schedule and available resources. Conclusion Test case prioritization has already been considerably discussed in the software testing domain. However, it is commonly learned that there are quite a number of existing prioritization techniques that can still be improved especially in data used and execution process for each approach.

Web engineering is a systematic approach to develop web applications, and numerous web engineering methods have been proposed. These methods were extended through defining new models by using different mechanisms to capture the web application concepts. Due to the complexity rising of web applications, the web engineering methods cannot provide web solutions anymore. Even though Interaction Flow Modeling Language (IFML) is recently proposed as a new method for developing web applications, it has limitations. Therefore these methods need to be improved. In this paper, we present the ability of IFML extensibility to support new concerns from web applications. Moreover, we extend IFML through UML mechanisms to support new concerns from the context to the user interface. The new IFML solves the lack of context web application through defining a new model and becomes a new direction to develop concerns modern web applications.

Regression test case selection techniques attempt to increase the testing effectiveness based on the measurement capabilities, such as cost, coverage, and fault detection. This systematic literature review presents state-of-the-art research in effective regression test case selection techniques. We examined 47 empirical studies published between 2007 and 2015. The selected studies are categorized according to the selection procedure, empirical study design, and adequacy criteria with respect to their effectiveness measurement capability and methods used to measure the validity of these results. The results showed that mining and learning-based regression test case selection was reported in 39% of the studies, unit level testing was reported in 18% of the studies, and object-oriented environment (Java) was used in 26% of the studies. Structural faults, the most common target, was used in 55% of the studies. Overall, only 39% of the studies conducted followed experimental guidelines and are reproducible. There are 7 different cost measures, 13 different coverage types, and 5 fault-detection metrics reported in these studies. It is also observed that 70% of the studies being analyzed used cost as the effectiveness measure compared to 31% that used fault-detection capability and 16% that used coverage.

In Model-based testing (MBT) for Software Product Lines (SPLs), many algorithms have been proposed for test case generation. The test case is generated based on a test model which aims to achieve optimization. The heuristic search algorithm is one of the techniques that can be used to traverse the test model with a good quality of solutions. This paper describes our experience in using three types of search algorithm, which are Floyd’s Warshall, Branch and Bound algorithm and Best First Search (FWA-BBA-BFS) which were integrated and hybridized in order to fully explore the test model. In this paper, this algorithm is validated based on test case results measured according to coverage criteria, generation time and size of test suite. Based on the experimental results, it is established that our proposed algorithm can generate test cases with reasonable cover-age, minimal execution time and appropriate size of test suite.

Model-driven code generation allows rapid generation of precise code thus reducing the development effort and the delivery time. Consequently, model-driven code generation has been a topic of interest in varying perspectives. While model-driven code generation has been explored well in many domains, its full potential has not been exploited in the context of aspect-oriented software development. The existing approaches have mainly focused on code generation from class diagrams only. Code generation from class diagrams is straightforward as majority of the constructs involved in these diagrams are directly mapped to those in programming languages. However, code generated using class diagrams is limited to skeletons of classes and methods only and does not contain behavior. In this study, we use the state diagrams and propose a mapping of its constructs to AspectJ language. We use the Reusable Aspect Models notation for this purpose. The approach addresses the mapping of both structure and behavior, however, owing to their strength with respect to modeling the system behavior, it essentially focuses on the state diagrams encapsulated in these models. A detailed mapping of different features of the state diagrams is proposed. The approach is illustrated by means of aspect models and corresponding mapped code from an aspect-oriented implementation of a Remote Service Caller example. The implementation shows that the approach can effectively be applied to obtain code for complete structure and behavior modeled using Reusable Aspect Models.