Towards Model Checking of Network Applications for IoT System Development

With the expansion of the Internet, Internet of Things (IoT) gains lots of interest from industries and academia. IoT applications enable human-to-device and device-to-device interactions. For a successful deployment of IoT systems and services, software reliability is a very important requirement for IoT to ensure that data/messages have been received and performed properly in a timely manner. The concurrent connections of embedded sensors and actuators are nondeterministic in nature which makes testing insufficient to guarantee program correctness. In contrast, model checking techniques explore the entire behavior of a system under test (SUT) in brute-force and systematic manner. It investigates each reachable state for different thread schedules. Recent model checking techniques have been applied directly to networked programs. This paper reviews model checking techniques for networked applications and presents their strengths and limitations. A preliminary proposal for model checking of networked applications that addresses the identified gap is presented

Feature Selection Method using Genetic Algorithm for Medical Dataset

There is a massive amount of high dimensional data that is pervasive in the healthcare domain. Interpreting these data continues as a challenging problem and it is an active research area due to their nature of high dimensional and low sample size. These problems produce a significant challenge to the existing classification methods in achieving high accuracy. Therefore, a compelling feature selection method is important in this case to improve the correctly classify different diseases and consequently lead to help medical practitioners. The methodology for this paper is adapted from KDD method. In this work, a wrapper-based feature selection using the Genetic Algorithm (GA) is proposed and the classifier is based on Support Vector Machine (SVM). The proposed algorithms was tested on five medical datasets naming the Breast Cancer, Parkinson’s, Heart Disease, Statlog (Heart), and Hepatitis. The results obtained from this work, which apply GA as feature selection yielded competitive results on most of the datasets. The accuracies of the said datasets are as follows: Breast Cancer - 72.71%, Parkinson’s – 88.36%, Heart Disease – 86.73%, Statlog (Heart) – 85.48 %, and Hepatitis – 76.95%. This prediction method with GA as feature selection will help medical practitioners to make better diagnose with patient’s disease. 

Adopting DevOps practices: an enhanced unified theory of acceptance and use of technology framework

DevOps software development approach is widely used in the software engineering discipline. DevOps eliminates the development and operations department barriers. The paper aims to develop a conceptual model for adopting DevOps practices in software development organizations by extending the unified theory of acceptance and use of technology (UTAUT). The research also aims to determine the influencing factors of DevOps practices’ acceptance and adoption in software organizations, determine gaps in the software development literature, and introduce a clear picture of current technology acceptance and adoption research in the software industry. A comprehensive literature review clarifies how users accept and adopt new technologies and what leads to adopting DevOps practices in the software industry as the starting point for developing a conceptual framework for adopting DevOps in software organizations. The literature results have formulated the conceptual framework for adopting DevOps practices. The resulting model is expected to improve understanding of software organizations’ acceptance and adoption of DevOps practices. The research hypotheses must be tested to validate the model. Future work will include surveys and expert interviews for model enhancement and validation. This research fulfills the necessity to study how software organizations accept and adopt DevOps practices by enhancing UTAUT

A Comparison of the Reliability Growth of Open Source and In-House Software

Abstrac

Enhancing manufacturing process by predicting component failures using machine learning

Manufacturers customize computer components upon receipt of sale orders. They perform burn-in tests on each unit of product before shipment to ensure a high standard of quality. Burn-in is normally associated with high production costs and slows down manufacturing operations. This study aims to enhance the manufacturing process by predicting test failure patterns using machine learning methods. By identifying the components that are likely to cause failures, manufacturers can accelerate the rectification process and improve delivery time which in turn leads to better customer service. This study hypothesized that the component of concern produces a higher test failure rate. To provide insight into the data and test the hypothesis, descriptive and predictive analytics are used at various stages. Predictive analytics was performed using machine learning via Naïve Bayes since it outperformed SVM and Random Forest classifier. For the descriptive analysis stage, a visual representation revealed many components (81) to be associated with a more than average test failure rate. Fisher’s exact test confirmed that 12 of them are statistically significant and worth studying their behaviour further. Moreover, an association rule mining exercise identified several combinations of modules that have a higher inclination with the test failure. For the predictive analytics stage, the Naïve Bayes classifier predicted test failure with 79% accuracy and 53% recall rate. Another Naïve Bayes classifier predicted error messages associated with a test failure with 68% recall rate over manually labelled error messages. However, a neural network-based automatic text classifier was developed and tested that yielded 66% accuracy. This analysis provides the foundation for a recommendation made that can reduce the burn test failure rate by 25% which is expected to increase further with the improved performance model upon training with a larger data set

An enhanced generic pipeline model for code clone detection

Maintainability is an important attribute when developing software. One of the factors that negatively affect maintainability of software is cloning. Cloning is identical copies of the same instances or fragments. Code cloning happens due to rapid changes when programmers perform clone instances and copy-paste technique. Although copy and paste is widely used in code reusability approach, it significantly increases maintenance cost. Current code clone research focuses in detection and analysis of code clones in order to help software developers to identify code clones in a source code and reuse the source code in order to decrease the maintenance cost. Necessary measures needed in order to reduce issues caused by cloning during implementation. Therefore, there is a need in exploring problems and possibilities associated in code cloning. This paper proposes an enhancement of a generic pipeline model for code clone detection. With the support of a tool, we implement and apply the proposed approach

Evolving Paper Based Activities Approach (EPAA) to promote interest in software engineering education

Software engineering education is vital as an introductory course in computer science or information technology undergraduate programmes. However, it seems to be dull to some educators to teach the concepts as compared to teach courses like programming and database. This phenomenon causes educators to have lack of interest in teaching and in turn affect the interest of learners to grasp the concepts better and relate it with other courses in computer science or information technology. This paper proposes an evolving paper-based activities approach (EPAA) to promote interest in software engineering education among both educators and learners. The approach aims to make software engineering education to be more interesting, engaging and integrated so that learners can appreciate why they learn software engineering course in computer science or information technology programmes. Two groups of students who took the related courses gave the positive feedbacks that the approach increased their interest in learning software engineering mainly in understanding the concept in object-oriented analysis and design using Unified Modeling Language

User-centered technique for managing and racking modification requests in prototype-based web applications

Nowadays, a lot of development of Web applications use prototyping approach such as throw-away prototyping and evolutionary prototyping. In order to fulfill the users' requirements, developers need to communicate with the users, therefore prototyping is widely used in software development to assist these developers. As we develop Web application and release them as a number of versions or releases of prototype, it constantly requires changes to evolve and meet users' specific requirements. Thus, prototyping approach may cause more maintenance cost to be incurred due to scope creep during software development. In addition, it provides the challenges to a maintenance team who is mostly not the actual development team, to manage and track maintenance process of such Web applications. This paper proposes a user-centered technique for managing and tracking modification requests in a Web application. We anticipate that the proposed technique can assist maintainers to manage and track modification of a Web application in more effective and efficient manner by capturing, classifying and validating the enhancement requests, problem report and modification requests directly from the users during the prototyping process itself. This in turn will avoid unexpected modification requests once we release the Web application and to allow the tracing of both enhancement and problem report during prototyping and modification requests and problem report during maintenance