Search

Wheelchair are designed to enhance a patient’s personal mobility. It should help users to engage as much as possible in events. Therefore, rather than having a negative impact on its health or safety, the consumer should be able to live more active lives. Researchers have constantly worked to provide the patient with the best tool. At the time, patients had the option to use a wheelchair only, but now exoskeletons were introduced and proved as planned to have been successful. However, the use of wheelchair have never been brought down, in fact, the wheelchair that we all known are evolving into something fresh and new due to the effort of researchers applying new technology. Since a new idea for wheelchairs has been proposed, we conducted a systematic mapping study (SMS) on features introduced by researchers on wheelchairs. We identified 41 papers coming from conferences, symposium, workshops and journals. A total of 13 features exist in wheelchairs are identified. We present a qualitative analysis of the wheelchair, an overview on features for wheelchairs.

Soft Computing based estimation by analogy is a lucrative research domain for the software engineering research community. There are a considerable number of models proposed in this research area. Therefore, researchers are of interest to compare the models to identify the best one for software development effort estimation. This research showed that most of the studies used mean magnitude of relative error (MMRE) and percentage of prediction (PRED) for the comparison of their estimation models. Still, it was also found in this study that there are quite a number of criticisms done on accuracy statistics like MMRE and PRED by renowned authors. It was found that MMRE is an unbalanced, biased, and inappropriate performance measure for identifying the best among competing estimation models. The accuracy statistics, e.g., MMRE and PRED, are still adopted in the evaluation criteria by the domain researchers, stating the reason for “widely used,” which is not a valid reason. This research study identified that, since there is no practical solution provided so far, which could replace MMRE and PRED, the researchers are adopting these measures. The approach of partitioning the large dataset into subsamples was tried in this paper using estimation by analogy (EBA) model. One small and one large dataset were considered for it, such as Desharnais and ISBSG release 11. The ISBSG dataset is a large dataset concerning Desharnais. The ISBSG dataset was partitioned into subsamples. The results suggested that when the large datasets are partitioned, the MMRE produces the same or nearly the same results, which it produces for the small dataset. It is observed that the MMRE can be trusted as a performance metric if the large datasets are partitioned into subsamples.

Currently, it is crucial to develop a complex software on time. Agile software development methodologies provide methods to develop a system in term of time and cost-saving but it has been criticized for software quality management. In this paper, a case study is used to find out the need of NFR change impact traceability approach in most of Agile software methodology. This case study was conducted in an undergraduate course that trained the students on how to develop software using Agile process model. This case study has been conducted for 4 months in an undergraduate-level course, Application Development. The samples of this case study are among Year 3 undergraduate students. The case study shows the lack of traceability techniques in the existing Agile process model (SFDD- Secured Feature Driven Development) that result to non-awareness of NFR change impact during development. Based on the case study mentioned the main objective of the case study conducted in survey is to empirically test the theoretical constructs and the hypothesized relationships of the research issues that concern on the lack of change impact management towards NFR in Agile Software Methodology. TANC (Traceability for Agile Non-Functional Requirement Change Impact) model offered techniques in tracing change impact during the agile development process. Therefore, the result of the case study, a traceability process model needs to design in order to tackle the NFR change impact issues in Agile software development.

Rich Internet Applications (RIAs) became to standard of interactive web applications on the internet fastly. It is a complex application with a rich user interface that distributed the data between client and server also allowing an asynchronous communication between them, but web engineering methods are not able to design and implement these features impeccably. The recent web engineering method is Interaction Flow Modeling Language (IFML) which adopted by Object Management Group (OMG). It has many features for developing interactions in web application compared with other web engineering methods but also has limitation for designing RIA features. In this paper, we enhance IFML method through extension the metamodels by using UML extension mechanism, in which, we define new IFML metamodel and some new elements to cope RIA features designing in data distribution between client and server. The results show that this enhancement enables IFML to develop the new types of web applications efficiently.

Many studies have been carried out in formulating software defect prediction but it is of limited knowledge that those studies emphasized on predicting defects in system testing phase. This study specifically focuses on establishing a prediction model for system testing defects by exploiting metrics prior to system testing under V-model development. The initiative helps independent testing team to prevent as many defects as possible from escaping to production environment. The proposed model analyzes development-related and testing-related metrics collected from requirement, design and construction phases in determining which of those could significantly predict defects at the start of system testing. By applying statistical analysis to those metrics, this model able to formulate one generalized mathematical equation for predicting defects in system testing. The model applies 95% prediction interval to ensure the accuracy of the prediction.

Fast growing of cities, urban places, and population presents major challenges in our daily lives. Finding proper solution from different perspectives of cities became a concern by both researchers and industries concern. Smart city (SC) is the answer to overcome this issue. Internet of thing (IoT) is a crucial part of SC which has a tremendous impact on all the cities’ sectors such as governance, health care, education, environment, and transportation. This paper provides a state of the art on Internet of thing (IoT)-based smart city (SC) from platform, architecture, application domain, and technology perspectives, also it concludes the relations between different technologies used in developing smart cities. Challenges and future trends of SC based on IoT will be discussed. As this research conducts, the SC solution based on IoT still confronts many challenges and problems which required further researches.

Real-Time Campus University Bus Tracking Mobile Application is a mobile application to help campus members detect the current location of the bus in real-time. Real-Time Campus University Bus Tracking Mobile Application is a hybrid mobile application. However, for this development, it is developed for Android user only. It can show updated estimation time arrival and the number of persons inside the bus. This project using two devices embedded inside the bus, which are GPS Tracker device and IoT people counter device. All devices will transmit the data into cloud database which is Firebase. Real-Time Campus University Bus Tracking Mobile Application is developed as a platform for user to receive the data transmitted from database. Other than that, Student will know the time arrival of the bus and the current quantity of people inside the bus to lead them avoid wasted time knowing that they wait for the bus that pack of passenger. The student also able to make complaint and feedback via the platform. Furthermore, this project using PhoneGap as a tool platform to develop the application. The GPS Tracker device using Arduino and IoT people counter using Raspberry PI to transmit data. Overall this project using the reusability techniques and Agile method to complete all the system which it is involved four interactions to make it full system work as expected.

With the evolution of modern web applications, several web engineering methods proposed to develop web applications. The modern web applications are; Rich Internet Application (RIA), Semantic Web Application (SWA), Ubiquitous Web Applications (UWA), and Intelligent Web Applications (IWA), with each of them having new features. The problem is that current web engineering methods cannot support new features of modern web applications. However, some of them extended for new concern of web applications but have limited, meaning these methods have a lack of adaptability to support features from modern web applications. In an attempt to solve this gap, we have defined a new adaptive model for the web engineering methods that can support the new features of modern web applications. This model very efficient in the process development and will be to increase the usability of the methods.

Software requirements engineering is a critical discipline in the software development life cycle. The major problem in software development is the selection and prioritization of the requirements in order to develop a system of high quality. This research analyzes the issues associated with existing software requirement prioritization techniques. One of the major issues in software requirement prioritization is that the existing techniques handle only toy projects or software projects with very few requirements. The current techniques are not suitable for the prioritization of a large number of requirements in projects where requirements may grow to the hundreds or even thousands. The research paper proposes an expert system, called the Priority Handler (PHandler), for requirement prioritization. PHandler is based on the value-based intelligent requirement prioritization technique, neural network and analytical hierarchical process in order to make the requirement prioritization process scalable. The back-propagation neural network is used to predict the value of a requirement in order to reduce the extent of expert biases and make the PHandler efficient. Moreover, the analytical hierarchy process is applied on prioritized groups of requirements in order to enhance the scalability of the requirement prioritization process.

Context: Cloud computing is a new computing technology that provides services to consumers and businesses. Due to the increasing use of these services, the quality of service (QoS) of cloud computing has become an important and essential issue since there are many open challenges which need to be addressed related to trust in cloud services. Many research issues have been proposed in QoS approaches in the cloud computing area.Objective: The aim of this study is to survey current research on QoS approaches in cloud computing in order to identify where more emphasis should be placed in both current and future research directions.Method: A systematic mapping study was performed to find the related literature, and 67 articles were selected as primary studies that are classified in relation to the focus, research type and contribution type.Result: The majority of the articles are of the validation research type (64%). Infrastructure as a service (48%) was the largest research focus area, followed by software as a service (36%). The majority of contributions concerned methods (48%), followed by models (32%).Conclusion: The results of this study confirm that QoS approaches in cloud computing have become an important topic in the cloud computing area in recent years and there remain open challenges and gaps which require future research exploration. In particular, tools, metrics and evaluation research are needed in order to provide useful and trustworthy cloud computing services that deliver appropriate QoS. 67 primary studies addressed QoS in cloud computing.The largest of studies discussed validation.The majority of studies considered infrastructure as service.Most studies focused on methods or models.QoS approaches require further research.

Web Engineering is a systematic approach for the development of web applications. In this area navigation model is defined as an important model in all the hypermedia and web engineering methods. Also homepage is one of the most important pages on the websites, because it is considered to be the key element that demonstrates the quality of websites. The web engineering methods still have weaknesses that are difficult for designers to design homepage's content perfectly. In this paper we compare navigation model between UML-Based Web Engineering (UWE) and Web Modeling Language (WebML). The purpose of this paper is to find a quality in these methods for the homepage's development, through designing navigation model in UWE and WebML, and comparing the elements of the navigation model. The result of this paper helps the designers to develop the homepages in order to solve of the weakness's navigation model.

Emerging technologies in education in these recent years have constantly impacted teaching and learning. One of those emerging technologies for teaching nowadays is using robotics. In the field of engineering and technology, programming is one of the most vital and challenging skills to learn. Programming can be tough for novices especially for those who have not acquired any basic programming skills. Robots have been used in the past few years to support the teaching of programming. Researchers have found that students are more interested in programming through robots utilization in teaching compared to traditional teaching methods. Therefore, this study has proposed a way for teaching and learning of programming to be more effective through adapting an existing Project-Oriented Problem-Based Learning (POPBL) framework. Most research works in the past have only implemented the teaching of programming using robotics through a Problem-Based Learning (PBL) framework without considering a systematic PBL framework. Thus, this work aims to study the effectiveness of using a mobile robot known as ‘RoboKar’ as a tool to support the learning of introductory programming by adapting an existing POPBL framework. The effectiveness of the adapted framework was assessed by evaluating three categories of learning comprising analytical and problem solving, learner's knowledge, as well as, collaborative learning. Our study results showed that by adapting a POPBL framework, students can acquire a better understanding in learning introductory programming concepts.

Two years ago, the Object Management Group (OMG) adopted a new standard method named Interaction Flow Modeling Language (IFML) for web engineering domain. IFML is designed to express the content, user interaction, and control behavior of the front end of applications. There are number lacks in web engineering methods, because each of them is defined to particular specifications, one of which is the open issue of supporting the whole lifecycle in process development. In this paper, we analyze IFML models in the process development lifecycle to show capability of the method used in the process development. We then make a comparison between IFML and other methods in lifecycle phases. Finally, we add IFML to the web engineering lifecycle’s map. It is anticipated that the result of this paper will be to become a guide for developers for using IFML in the development of new applications.

There are two major strategies to schedule real-time tasks in multiprocessor systems; partitioning and global scheduling. The partitioning approach has acceptable overhead but cannot guarantee to be optimal. The global approach can provide this guarantee but it has considerable overhead. Thus, a multiprocessor real-time scheduling approach for weakly hard real-time tasks is proposed that employs hybrid scheduling. Studies have shown that current multiprocessor scheduling of weakly hard real-time tasks used imprecise computation model based on iterative algorithms. This algorithm decomposed into two parts; mandatory and optional, unfortunately, the result analysis is precise only if its mandatory and optional parts are both executed. Even, the use of hierarchical scheduling algorithm, such as two-level scheduling under PFair algorithm may cause high overhead due to frequent preemptions and migrations. In this paper, an alternative scheduling approach will be proposed, which is, its combines elements of the two well-known multiprocessor scheduling approaches. It aims to employs benefits and advantages of the partitioning and global scheduling. Accordingly, the proposed hybrid multiprocessor real-time scheduling is use the best algorithm of each of partitioning and global approaches, R-BOUND-MP-NFRNS and RM-US (m/3m−2) with multiprocessor response time test. Schedulability experiments and simulation results using Matlab show the proposed hybrid multiprocessor scheduling approach to be effective for weakly hard real-time tasks.

In the value-based software (VBS) development, an innovative idea is realised in order to gain an economic leverage. The VBS systems deal with financial streams and this thing make them different from the conventional systems. The success of a VBS system is associated with a valuable set of requirements. The valuable requirements can only be gathered from success critical stakeholders. To select a set of success critical stakeholders, different stakeholders identification and quantification (SIQ) approaches are presented by the researchers. The current approaches cannot be adopted as a standard as different methods and processes are adopted in different approaches. In this study, the aim is to find out the reported evidence based attributes or characteristics of the stakeholders and their usage context in terms of their application in different domains, stakeholders' quantification metrics, the reported stakeholder types and the reported issues of VBS development. The standard systematic literature review guidelines are used as given by Barbara Kitchenham. The literature evidence shows that there is a need to explore all the possible stakeholders' attributes. The stakeholders' metrics can be derived by using the stakeholders' attributes and a new SIQ framework can be proposed for the VBS systems.

The purpose of a metamodel in web engineering methodologies is for the platform independent analysis as well as the design of the content, navigation, and presentation issues of web applications. In the previous years, numbers of methodologies for the development of web applications were proposed, and most of them defined their notation for creating metamodels. The increasing expansion and complexity of web applications are a new challenge for web software developers. This paper presents a comparison study between metamodel of the three methodologies which are; UML-Based Web Engineering (UWE), Web Modeling Language (WebML) and Object Oriented Hypermedia (OOH). The aim is to show the capability of the methodologies to address the challenges in developing the web applications. The evaluation results presented in this paper help the designer in providing initial knowledge of the strengths and weaknesses of the three methodologies for developing web applications.

In this paper a systematic review is conducted to investigate the structure, components, techniques, evaluation procedure, and comparison scope related to prior ABE-based studies. The undeniable role of accurate development effort estimation in the success of software project management has attracted the attention of researchers over the past few years. Among various algorithmic and non-algorithmic estimation methods, analogy based estimation (ABE) is a widely accepted method due to its simplicity and estimation capability. This paper investigates the improvement process of ABE method during 2000 to 2012. Six research questions are defined to be addressed through evaluation of prior ABE-based studies. The review domain includes 24 papers selected through a tough filtration process. The results show that improvement of ABE can be performed through adjustment, grey theory, attribute weighting and attribute selection techniques. Moreover, ABE configurations can significantly affect the results.

This paper proposes an improved Latent Semantic Indexing (LSI) with Term Frequency-Inverse Document Frequency (TFIDF) variant for software traceability. The main advantage of the method is the simplicity and accuracy of the algorithm such that it can produce high precision and recall. The accuracy of tracing precise links between software artefacts is achieved by feeding the LSI with TF-IFDF variant that halves the TF and IDF values, thus increases the probability of yielding precise links. To test the accuracy of the proposed method, two case studies were evaluated, namely Mushroom Management System (MMS) and Robotic Wheelchair System (RWS). The superiority of the proposed method over a conventional LSI is confirmed by 409.09%, 900.00%, and 620.00% improvements for precision, recall, and harmonic mean scores, respectively, in MMS case study, at cosine threshold of 0.94. It is anticipated that the method could be beneficial in the design of a practical and accurate system for retrieving precise software traceability links.

The integration of aspect oriented modeling approaches with model-driven engineering process achieved through their direct transformation to aspect-oriented code is expected to enhance the software development from many perspectives. This study aims to assess the existing UML-based aspect-oriented modeling techniques from the perspective of their suitability with regards to integration into model-driven engineering process through aspect-oriented code generation. For this purpose, we selected two mature aspectoriented modeling approaches, Reusable Aspect Models and Theme/UML, and proceeded to evaluate them in a detailed way from the specific perspectives of design and its mapping to the implementation code. The in-depth comparison reveals some points equally shared by both approaches, and identifies some areas where one has advantage over the other. The study concludes that the Reusable Aspect Models approach may be seen as a preferred approach to handling the task of integration using aspect-oriented code generation.

Accurate estimation of software development effort is strongly associated with the success or failure of software projects. The clear lack of convincing accuracy and flexibility in this area has attracted the attention of researchers over the past few years. Despite improvements achieved in effort estimating, there is no strong agreement as to which individual model is the best. Recent studies have found that an accurate estimation of development effort in software projects is unreachable in global space, meaning that proposing a high performance estimation model for use in different types of software projects is likely impossible. In this paper, a localized multi-estimator model, called LMES, is proposed in which software projects are classified based on underlying attributes. Different clusters of projects are then locally investigated so that the most accurate estimators are selected for each cluster. Unlike prior models, LMES does not rely on only one individual estimator in a cluster of projects. Rather, an exhaustive investigation is conducted to find the best combination of estimators to assign to each cluster. The investigation domain includes 10 estimators combined using four combination methods, which results in 4017 different combinations. ISBSG, Maxwell and COCOMO datasets are utilized for evaluation purposes, which include a total of 573 real software projects. The promising results show that the estimate accuracy is improved through localization of estimation process and allocation of appropriate estimators. Besides increased accuracy, the significant contribution of LMES is its adaptability and flexibility to deal with the complexity and uncertainty that exist in the field of software development effort estimation.

The development complexity of Distributed Embedded Real-Time Systems (DERTS) can be reduced by the use of Service-Oriented Computing (SOC). However, the existing modeling methods allow modeling of either DERTS or SOC concepts and there is a lack of meta-model for Service-Oriented development of DERTS. This paper proposes a service-based meta-model for DERTS, along with the constraints of the elements of the meta-model. A Smart Home case study was designed to validate the meta-model. This meta-model could not only be beneficial for Service-Oriented development of DERTS, but can also be used at the Platform Independent Model (PIM) level of MDD.

The estimation of software development effort has been centralized mostly on the accuracy of estimates through dealing with heterogeneous datasets regardless of the fact that the software projects are inherently complex and uncertain. In particular, Analogy Based Estimation (ABE), as a widely accepted estimation method, suffers a great deal from the problem of inconsistent and non-normal datasets because it is a comparison-based method and the quality of comparisons strongly depends on the consistency of projects. In order to overcome this problem, prior studies have suggested the use of weighting methods, outlier elimination techniques and various types of soft computing methods. However the proposed methods have reduced the complexity and uncertainty of projects, the results are not still convincing and the methods are limited to a special domain of software projects, which causes the generalization of methods to be impossible. Localization of comparison and weighting processes through clustering of projects is the main idea behind this paper. A hybrid model is proposed in which the software projects are divided into several clusters based on key attributes (development type, organization type and development platform). A combination of ABE and Particle Swarm Optimization (PSO) algorithm is used to design a weighting system in which the project attributes of different clusters are given different weights. Instead of comparing a new project with all the historical projects, it is only compared with the projects located in the related clusters based on the common attributes. The proposed method was evaluated through three real datasets that include a total of 505 software projects. The performance of the proposed model was compared with other well-known estimation methods and the promising results showed that the proposed localization can considerably improve the accuracy of estimates. Besides the increase in accuracy, the results also certified that the proposed method is flexible enough to be used in a wide range of software projects.

Context: Model-driven code generation is being increasingly applied to enhance software development from perspectives of maintainability, extensibility and reusability. However, aspect-oriented code generation from models is an area that is currently underdeveloped. Objective: In this study we provide a survey of existing research on aspect-oriented modeling and code generation to discover current work and identify needs for future research. Method: A systematic mapping study was performed to find relevant studies. Classification schemes have been defined and the 65 selected primary studies have been classified on the basis of research focus, contribution type and research type. Results: The papers of solution proposal research type are in a majority. All together aspect-oriented modeling appears being the most focused area divided into modeling notations and process (36%) and model composition and interaction management (26%). The majority of contributions are methods. Conclusion: Aspect-oriented modeling and composition mechanisms have been significantly discussed in existing literature while more research is needed in the area of model-driven code generation. Furthermore, we have observed that previous research has frequently focused on proposing solutions and thus there is need for research that validates and evaluates the existing proposals in order to provide firm foundations for aspect-oriented model-driven code generation.

Healthcare is one of the core areas in medical domain. In healthcare the data exist in various forms like respiration data, blood pressure readings, prescriptions and others. The data may help in decision-making for different initiatives in order to provide better healthcare services. However, in order to make this possible there is a need to diagnose the data in a professional way. Currently, there is a lack of a system or way which may help in decision-making in big data analysis in the form of phases. In this research, a framework has been proposed to diagnose the healthcare data for efficient data analysis.

Nowadays, since business environment is highly dynamic, software necessities are continuously being improved in order to meet the needs of modern industrialized world. Therefore, IT organizations seek for a quick way of software delivery and for adapting to the necessary technological changes. From this ideal viewpoint, traditional plan-driven developments lag behind to overcome these conflicts. The purpose of this chapter is to present the existing models and frameworks which guide organizations to adopt agile methods. This may help organizations to follow professionals' suggestions during their migration from traditional systems to agile development.

Development effort is one of the most important metrics that must be estimated in order to design the plan of a project. The uncertainty and complexity of software projects make the process of effort estimation difficult and ambiguous. Analogy-based estimation (ABE) is the most common method in this area because it is quite straightforward and practical, relying on comparison between new projects and completed projects to estimate the development effort. Despite many advantages, ABE is unable to produce accurate estimates when the importance level of project features is not the same or the relationship among features is difficult to determine. In such situations, efficient feature weighting can be a solution to improve the performance of ABE. This paper proposes a hybrid estimation model based on a combination of a particle swarm optimization (PSO) algorithm and ABE to increase the accuracy of software development effort estimation. This combination leads to accurate identification of projects that are similar, based on optimizing the performance of the similarity function in ABE. A framework is presented in which the appropriate weights are allocated to project features so that the most accurate estimates are achieved. The suggested model is flexible enough to be used in different datasets including categorical and non-categorical project features. Three real data sets are employed to evaluate the proposed model, and the results are compared with other estimation models. The promising results show that a combination of PSO and ABE could significantly improve the performance of existing estimation models.

Regression testing of a large test pool consistently needs a prioritization technique that caters requirements changes. Conventional prioritization techniques cover only the methods to find the ideal ordering of test cases neglecting requirement changes. In this paper, we propose string dissimilarity-based priority assignment to test cases through the combination of classical and non-classical textual comparison metrics and elaborate a prioritization algorithm considering requirement changes. The proposed technique is suitable to be used as a preliminary testing when the information of the entire program is not in possession. We performed evaluation on random permutations and three textual comparison metrics and concluded the findings of the experiment.

Real-time tasks can be classified into three categories, based on the “seriousness” of deadline misses - hard, soft and weakly hard real-time tasks. The consequences of a deadline miss of a hard real-time task cannot be accepted whereas soft real-time tasks tolerate “some” deadline misses. While, in a weakly hard real-time task, the distribution of its met and missed deadlines is stated and specified precisely. Due to the complexity and significantly increased functionality in system computation, attention has been given to multiprocessor scheduling, comprised of several processors. Due to the fact that in multiprocessor, there have more than one processor, algorithms which can cater higher computational complexity for task allocation and for task migration are highly required. Thus, the sufficient and efficient scheduling algorithm supported by accurate schedulability analysis technique is presented to provide weakly hard real-time guarantees. In this paper, a schedulability analysis to schedule weakly hard real-time tasks has been proposed by using the global multiprocessor scheduling technique, called multiprocessor response time analysis combining with the exact analysis, named hyperperiod analysis and deadline models; weakly hard constraints and μ-pattern under static priority scheduling. Then, the Matlab simulation tool is used in order to validate the result of analysis. From the performance evaluation results, it proved that the proposed approach is satisfied the tasks deadlines with less number of misses.

Reliability is a software quality characteristic that refer to the probability a system will work correctly over a period of time. Reliability prediction is important as it can be used to plan deployment, maintenance and test activities. This study assesses the efficiency of several techniques in software reliability model (SRM) selection and aims to find out the possible enhancement to improve software reliability accuracy. The result of the survey shows most of the SRM selection technique does not optimizes the model parameter.

Nowadays, software requirements are widely increasing and modern software technologies are rapidly growing. Therefore, planning and managing the software projects are more important as compared to the past. Unsuitable project planning has been the main reason of software project fails in recent years. Estimating is performed at the early stages of project and it is one of the most important activities in software project planning. During the recent years many different methods have been proposed to estimate the software metrics. Software development effort and software size are most important metrics in this field. Since uncertain nature of software projects makes it difficult to estimate the metrics, soft computing techniques have been widely used in software metrics estimation. These techniques can improve the accuracy of estimations in software projects by means of neural networks, genetic algorithm, fuzzy logic and so on. Among all mentioned methods, due to high flexibility and adaptability, neural networks have been used more than the other methods. A comparative study can be useful to discriminate the performance of neural networks regarding the software metrics estimation. In this paper 15 previous research works in term of using neural networks for software estimation were investigated basically. Analyzing the selected research works showed that neural networks can estimate the software metrics more accurate than most common algorithmic methods.

Rapid Quality assurance is an important element in software testing in order to produce high quality products in Software Product Line (SPL). One of the testing techniques that can enhance product quality is Model-Based Testing (MBT). Due to MBT effectiveness in terms of reuse and potential to be adapted, this technique has become an efficient approach that is capable to handle SPL requirements. In this paper, the authors present an approach to manage variability and requirements by using Feature Model (FM) and MBT. This paper focuses on modelling the integration towards enhancing product quality and reducing testing effort. Further, the authors considered coverage criteria, including pairwise coverage, all-state coverage, and all-transition coverage, in order to improve the quality of products. For modelling purposes, the authors constructed a mapping model based on variability in FM and behaviour from statecharts. The proposed approach was validated using mobile phone SPL case study.

Software Product Line (SPL) describes procedures, techniques, and tools in software engineering by using a common method of production for producing a group of software systems that identical from a shared set of software assets. In SPL, the similarity-based prioritization can resemble combinatorial interaction testing in scalable and efficient way by choosing and prioritize configurations that most dissimilar. However, the similarity distances in SPL still not so much cover the basic detail of feature models which are the notations. Plus, the configurations always have been prioritized based on domain knowledge but not much attention has been paid to feature model notations. In this paper, we proposed the usage of mandatory and optional notations for similarity distances. The objective is to improve the average percentage of faults detected (APFD). We investigate four different distances and make modifications on the distances to increase APFD value. These modifications are the inclusion of mandatory and optional notations with the similarity distances. The results are the APFD values for all the similarity distances including the original and modified similarity distances. Overall, the results shown that by subtracting the optional notation value can increase the APFD by 3.71% from the original similarity distance.

Massive Open Online Courses MOOCs have become more acceptable as a learning program globally, including Malaysia. One main issue that has been discussed since the implementation of MOOCs is the issue of low student retention or high dropout rates from the course. Various factors have been found to play a role in this issue including the interaction factor. Previous studies have experimented with various strategies to monitor student retention and apply intervention programs to improve the situation. The strategies include the usage of machine learning and data mining techniques in analysing students’ online interactions to predict student retention rates. The implementation of these strategies produced promising result. However, in Malaysia, these strategies are not really implemented yet. Therefore, this paper discusses the issue of student retention in MOOCs, explores possible intervention plans using data mining and its suitability with the current platforms used for MOOCs. The proposed method includes predictive analytics that involves classification analysis. This paper suggests that the method can be applied to the current platform and complement intervention programs for the issue of low retention or high dropouts with several improvements.

Software Product Line (SPL) is an effective approach in software reuse in which core assets can be shared among the members of the product line with an explicit treatment of variability. Core assets, which are developed for reuse in domain engineering, are selected for product specific derivation in application engineering. Decision making support during product derivation is crucial to assist in making multiple decisions during product specific derivation. Multiple decisions are to be resolved at the architectural level as well as the detailed design level, address the need for assisting the decision making process during core asset derivation. Architectural level decision making is based on imprecise, uncertain and subjective nature of stakeholder for making architectural selection based on non- functional requirements (NFR). Furthermore, detail design level involves the selection of suitable features which have the rationale behind each decision. The rationale for the selection, if not documented properly, will also result in loss of tacit knowledge. Therefore, a multi-attribute architecture design decision technique is proposed to overcome the above mentioned problem. The technique combines Fuzzy Analytical Hierarchy Process (FAHP) with lightweight architecture design decision documentation to support the decision making during core asset derivation. We demonstrate our approach using the case study of Autonomous Mobile Robot (AMR). The case study implementation shows showed that the proposed technique supports software engineer in the process of decision making at the architecture and detail design levels.

System scenarios derived from requirements specification play an important role in the early software reliability engineering. A great deal of research effort has been devoted to predict reliability of a system at early design stages. The existing approaches are unable to handle scalability and calculation of scenarios reliability for large systems. This paper proposes modeling of scenarios in a scalable way by using a scenario language that describes system scenarios in a compact and concise manner which can results in a reduced number of scenarios. Furthermore, it proposes a calculation strategy to achieve better traceability of scenarios, and avoid computational complexity. The scenarios are pragmatically modeled and translated to finite state machines, where each state machine represents the behaviour of component instance within the scenario. The probability of failure of each component exhibited in the scenario is calculated separately based on the finite state machines. Finally, the reliability of the whole scenario is calculated based on the components’ behaviour models and their failure information using modified mathematical formula. In this paper, an example related to a case study of an automated railcar system is used to verify and validate the proposed strategy for scalability of system modeling.

Real-time systems or tasks can be classified into three categories, based on the “seriousness” of deadline misses – hard, soft and weakly hard real-time tasks. The consequences of a deadline miss of a hard real-time task can be prohibitively expensive because all the tasks must meet their deadlines whereas soft real-time tasks tolerate “some” deadline misses. Meanwhile, in a weakly hard real-time task, the distribution of its met and missed deadlines is stated and specified precisely. As real-time application systems increasingly come to be implemented upon multiprocessor environments, thus, this study applies multiprocessor scheduling approach for verification of weakly hard real-time tasks and to guaranteeing the timing requirements of the tasks. In fact, within the multiprocessor, the task allocation problem seem even harder than in uniprocessor case; thus, in order to cater that problem, the sufficient and efficient scheduling algorithm supported by accurate schedulability analysis technique is present to provide weakly hard real-time guarantees. In this paper, a weakly hard scheduling approach has been proposed and schedulability analysis of proposed approach consists of the partitioned multiprocessor scheduling techniques with solutions for the bin-packing problem, called R-BOUND-MP-NFRNS (R-BOUND-MP with next-fit-ring noscaling) combining with the exact analysis, named hyperperiod analysis and deadline models; weakly hard constraints and µ-pattern under static priority scheduling. Then, Matlab simulation tool is used in order to validate the result of analysis. From the evaluation results, it can be proven that the proposed approach outperforms the existing approaches in terms of satisfaction of the tasks deadlines.

Software behavioural models that derive from early requirements specifications such as use-case scenarios and properties have proven useful in early analysis and checking of the design correctness of individual components or whole system. However, deriving of these models becomes harder as a system specification grows. Expressive scenario description language that able to compact and concise scenario specifications is one of the solutions can enhance the ability of modeling large requirements specifications. Deriving behaviour models from compacted scenario specifications is a challenge related to this solution. This paper, introduce expressive scenario language and outline a process to derive system behavioural models from scenarios of this language in form of Labelled Transition Systems (LTS). In addition, the paper also covers some discussion of how these derived models can help analysis of software quality attributes.

This paper reports results of an action research conducted in an outreach program aimed at introducing secondary school students to the exciting world of computer programming. The key challenges were to introduce programming and problem solving session that can be done in a very short time (within 1 to 2 hours) and in a fun way. This was achieved by using a physical mobile robot called RoboKar to engage students with the problem solving activities involving programming. From the entry and exit surveys, and facilitators' observations, it can be concluded that the robot platform and the outreach program were able to introduce and spark interest in learning programming among the school students.

In requirements engineering (RE), different activities are carried on to add up business values to the software products. In value-base software (VBS), financial impacts on business are measured, and this phenomenon makes VBS different than other traditional software applications. In VBS systems, determination of valuable requirements is a crucial task and depends on multiple aspects or factors. Requirement prioritization is a part of RE and is considered as a crucial decision-making activity. Requirements prioritization is a process where requirements are arranged in an order to create a stakeholder priority list. Different requirements prioritization techniques are used by the experts in the industry to partially support the prioritization process for VBS development. Business and technical aspects play a vital role to make prioritization process effective. In existing requirements prioritization techniques, technical aspects are not applied well, and their consideration is partial only. Moreover, the business aspects are ignored which are highly vital for VBS development. There is a need to introduce multi-aspects requirements prioritization technique to support the selection of candidate requirements for VBS development. Hence, this paper focuses on a new requirements prioritization technique for VBS.

Aspect-oriented design notations improve the reusability and maintainability of models by means of separating crosscutting concerns from the core functionality of the system. However, as different design notations propose dissimilar ways of modeling and composing the crosscutting and core concerns, the capabilities of aspect design notations with regards to producing highly reusable and maintainable models may also vary. In this paper, we apply a selected set of aspect modeling notations to a modeling scenario and evaluate them with respect to reusability and maintainability of the obtained models. For this purpose, we have used different metrics related to well-known software engineering attributes that affect the reusability and maintainability of produced design. The results reveal that the Reusable Aspect Models performs well against majority of the employed metrics.

Software development process (SDP) and Software products are like two sides of a coin. We cannot achieve one without another. Today, in our software industries, monitoring software process is very challenging. Many problems of software process monitoring are hampering the quality of our software products. Several researchers in this area contributed their quota on addressing process monitoring issues using quantitative techniques. In this study, we address the problem of detecting software process deviations as a result of variations, investigating the causes of variations in software process, and the problem of process measurement. In addition, the study focus on code peer review process (CPRP). The first two problems can be addressed using one of the powerful quantitative techniques known as statistical process control (SPC). Also, control charts would be used in this study as it has been proved to be one of the suitable tools of SPC in monitoring process issues. As we know, the more defects we found during SDP, the less quality of the software product. Therefore, this study considers defect density as the metric to be use due to its significance in determining product quality. In order to have good analysis, this study conduct a case study on both Capability Maturity Model (CMM), lower and higher maturity levels software industries. On the other hand, to handle the problem of process measurement, a Sequential Strategy for Process Measure (SSPM) is proposed. This strategy is evaluated by Instrument for Evaluating Software Measurement Repository (IESMR) and Normative Information Model-based System Analysis and Design (NIMSAD) framework. Based on its evaluation, the strategy is similar to IESMR but differ in selecting measures, therefore it can be use for process measurement.

Requirements engineering (RE) in software development life cycle is considered as a main pillar of success of a software system. RE process is used to find out the key requirements in order to develop a system as per needs of the stakeholders. The two focal points in RE process are stakeholders and requirements. Systematic literature review (SLR) is the mean of collecting evidence-based data about the key areas of a research topic and its related issues. In this paper, a study of existing SLRs on RE is conducted in order to find out the key intricacies dealt in the domain of RE. The main purpose of this study is to aggregate the knowledge in the domain of software RE as elicited in different SLRs about different RE problems.

System scenarios that derived from system design specification play an important role in the reliability engineering of component-based software systems. A great deal of research effort has been devoted to predict the reliability of a system at the design-time, only few papers address the challenges of modeling and calculation of scenarios reliability. This paper identifies various challenges related to scenario modeling at the early design stages based on software architecture specification. It discusses the truncation and modeling methods of scenarios, the related computation cost, and their effect on the approach scalability, which has been missed by most of the current approaches. The paper proposes a modeling and calculation approach that, pragmatically, models and divides scenarios by a scenario specification language and finite state machine. It utilizes mathematical formulas for the calculations. The proposed work can be used as part of the reliability prediction approach to model and calculate the reliability of scenarios. We demonstrate the applicability of our approach by modeling and calculating scenarios reliability of an automated railcar system.

The importance of missing a deadline is one of the classifications of a real-time tasks or systems. A weakly hard real-time system is a new generation for a real-time system in which some degree of missed or losses deadlines are tolerated occasionally but the missing of deadlines has to be stated precisely. For a traditional real-time system, timing requirements in hard real-time are very restrictive, must meet all the tasks deadlines. Meanwhile, in soft real-time timing requirements, the requirements are too relaxed because there are no guarantees can be given for the deadline, whether it is met or missed. As a systems demand complex and significantly increased functionality, multiprocessor scheduling has been given attention and taken into consideration. In fact, within the multiprocessor, the predictability problems seem even harder. Thus, in order to deal with the problem, the partitioned multiprocessor scheduling techniques with solutions for the bin-packing problem, named R- BOUND-MP-NFRNS (R-BOUND-MP with next-fit-ring noscaling) combining with the exact analysis, called hyperperiod analysis and deadline models; weakly hard constraints and µ- pattern are present to provide weakly hard real-time guarantees under static priority scheduling algorithm. We prove that the proposed approach provides predictable weakly hard real-time tasks through validation and simulation results.

In recent years, reliability assessment is an essential process in system quality assessments. However, the best practice of software engineering for reliability analysis is not yet of its matured stage. The existing works are only capable to explicitly apply a small portion of reliability analysis in a standard software development process. In addition, an existing reliability assessment is based on an assumption provided by domain experts. This assumption is often exposed to errors. An effective reliability assessment should be based on reliability requirements that could be quantitatively estimated using metrics. The reliability requirements can be visualized using reliability model. However, existing reliability models are not expressive enough and do not provide consistence-modeling mechanism to allow developers to estimate reliability parameter values. Consequently, the reliability estimation using those parameters is usually oversimplified. With this situation, the inconsistency problem could happen between different estimation stages. In this chapter, a new Model-Based Reliability Estimation (MBRE) methodology is developed. The methodology consists of reliability model and reliability estimation model. The methodology provides a systematic way to estimate system reliability, emphasizing the reliability model for producing reliability parameters which will be used by the reliability estimation model. These models are built upon the timing properties, which is the primary input value for reliability assessment.

Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction.