Supporting inheritance hierarchy changes in model-based regression test selection

Models can be used to ease and manage the development, evolution, and runtime adaptation of a software system. When models are adapted, the resulting models must be rigorously tested. Apart from adding new test cases, it is also important to perform regression testing to ensure that the evolution or adaptation did not break existing functionality. Since regression testing is performed with limited resources and under time constraints, regression test selection (RTS) techniques are needed to reduce the cost of regression testing. Applying model-level RTS for model-based evolution and adaptation is more convenient than using code-level RTS because the test selection process happens at the same level of abstraction as that of evolution and adaptation. In earlier work, we proposed a model-based RTS approach called MaRTS to be used with a fine-grained model-based adaptation framework that targets applications implemented in Java. MaRTS uses UML models consisting of class and activity diagrams. It classifies test cases as obsolete, reusable, or retestable based on changes made to UML class and activity diagrams of the system being adapted. However, MaRTS did not take into account the changes made to the inheritance hierarchy in the class diagram and the impact of these changes on the selection of test cases. This paper extends MaRTS to support such changes, and demonstrates that the extended approach performs as well as or better than code-based RTS approaches in safely selecting regression test cases. While MaRTS can generally be used during any model-driven development or model-based evolution activity, we have developed it in the context of runtime adaptation. We evaluated the extended MaRTS on a set of applications, and compared the results with code-based RTS approaches that also support changes to the inheritance hierarchy. The results showed that the extended MaRTS selected all the test cases relevant to the inheritance hierarchy changes, and that the fault detection ability of the selected test cases was never lower than that of the baseline test cases. The extended MaRTS achieved comparable results to a graph-walk code-based RTS approach (DejaVu), and showed a higher reduction in the number of selected test cases when compared with a static analysis code-based RTS approach (ChEOPSJ)

Performance Evaluation of Optimized Predictive Model for Software Defined Network Traffic Management using Machine Learning

Communication channel is essential in any type of engagement for delivering and receiving data via the internet. To determine the most efficient and safe way through which network data may travel while minimizing the danger of network breaches or cyber-attacks. The objective is to build an optimized network traffic management predictive model that can predict the ideal path in real-time while accounting through the dynamic nature of software defined network traffic and the continuously changing danger of landscaping. To design a robust model of the data and scalable system that can suggest accurate suggestions of route to the network managers, a thorough grasp of network’s infrastructure, data analysis, and machine learning techniques are applied. Choosing the optimum path route data from the sdn based network traffic dataset, the model suggests an optimal path to avoid network communication traffic and congestion. Here nine Machine Learning algorithms are explored and analysed their performance by using the percentage split, resampling and cross validation which originally recorded as 92.76% and after training with cross validation it improved to 98.40% providing the best optimal path with minimum congestions. Building the optimized network traffic management model not only provide network security but also contribute to environmental sustainability. Their capacity to properly filter and manage network traffic helps to decrease energy usage by predicting the optimal routes for software defined network traffic

A Machine Learning Approach for Intrusion Detection

Master's thesis in Information- and communication technology (IKT590)Securing networks and their confidentiality from intrusions is crucial, and for this rea-son, Intrusion Detection Systems have to be employed. The main goal of this thesis is to achieve a proper detection performance of a Network Intrusion Detection System (NIDS). In this thesis, we have examined the detection efficiency of machine learning algorithms such as Neural Network, Convolutional Neural Network, Random Forestand Long Short-Term Memory. We have constructed our models so that they can detect different types of attacks utilizing the CICIDS2017 dataset. We have worked on identifying 15 various attacks present in CICIDS2017, instead of merely identifying normal-abnormal traffic. We have also discussed the reason why to use precisely this dataset, and why should one classify by attack to enhance the detection. Previous works based on benchmark datasets such as NSL-KDD and KDD99 are discussed. Also, how to address and solve these issues. The thesis also shows how the results are effected using different machine learning algorithms. As the research will demon-strate, the Neural Network, Convulotional Neural Network, Random Forest and Long Short-Term Memory are evaluated by conducting cross validation; the average score across five folds of each model is at 92.30%, 87.73%, 94.42% and 87.94%, respectively. Nevertheless, the confusion metrics was also a crucial measurement to evaluate the models, as we shall see. Keywords: Information security, NIDS, Machine Learning, Neural Network, Convolutional Neural Network, Random Forest, Long Short-Term Memory, CICIDS2017

Cyber Security Network Anomaly Detection and Visualization

This MQP presents a novel anomaly detection system for computer network traffic, as well as a visualization system to help users explore the results of the anomaly detection. The detection algorithm uses a novel approach to Robust Principal Component Analysis, to produce a lower dimensional subspace of the original data, for which a random forest can be applied to predict anomalies. The visualization system has been designed to help cyber security analysts sort anomalies by attribute and view them in the context of normal network activity. The system consists of an overview of firewall logs, a detail view of each log, and a feature view where an analyst can see which features of the firewall log were implicated in the anomaly detection algorithm

Cyber Security Network Anomaly Detection and Visualization

This MQP presents a novel anomaly detection system for computer network traffic, as well as a visualization system to help users explore the results of the anomaly detection. The detection algorithm uses a novel approach to Robust Principal Component Analysis, to produce a lower dimensional subspace of the original data, for which a random forest can be applied to predict anomalies. The visualization system has been designed to help cyber security analysts sort anomalies by attribute and view them in the context of normal network activity. The system consists of an overview of firewall logs, a detail view of each log, and a feature view where an analyst can see which features of the firewall log were implicated in the anomaly detection algorithm

Suorituksen kattavuustietojen käyttämiseen perustuva testien valintatapa Python-ohjelmille

Regression testing is a type of testing that aims to verify that the existing test suite will not find any defects in a modified program. Regression tests are usually run after each program modification and may take lots of processing time to complete. Regression test selection is a process where only a relevant subset of tests are selected from the test suite for execution with the goal of reducing the time the regression test execution takes. Safe regression test selection methods are one that can prove that none of the deselected test cases would have found any defects, so that running them is not necessary. Researchers have proposed multiple different methods for safe and unsafe regression test selection. Many of them require control flow graph or similar information that can be extracted during code compilation step. Therefore most of these methods are unsuitable for dynamically typed programming languages where that information can not be extracted. This thesis presents a test coverage based regression test selection method that can be used with interpreted programming languages. The presented method does not require any changes to tested program's source code. The presented method's test selection precision was tested with existing medium sized proprietary web application, and the results are somewhat mixed. The overhead imposed by the coverage based test selection method increased the test suite's execution time significantly. The test selection method managed to select a small subset of the test suite roughly half of the time. In the other half of the time the test selection had to re-run all tests.Regressiotestaus on testauksen muoto, jonka tarkoituksena on varmistaa, että ohjelman olemassa olevat testit eivät löydä virheitä muokatusta ohjelmasta. Regressiotestaus suoritetaan yleensä jokaisen ohjelman muutoksen jälkeen, ja sen suoritus voi viedä paljon prosessointiaikaa. Regressiotestien valinta on prosessi, jossa ohjelman kaikkien testien joukosta valitaan muutoksen kannalta oleellinen testien alijoukko. Valinnan tavoitteena on pienentää testien määrää ja näin vähentää testien suoritukseen kuluvaa aikaa. Turvalliset regressiotestien valintamenetelmät ovat menetelmiä jossa voidaan todistaa, että valitsemattomat testit eivät olisi voineet löytää virheitä, ja täten ne voidaan jättää suorittamatta. Tutkijat ovat kehitelleet useita eri menetelmiä turvalliseen ja epäturvalliseen regressiotestien valintaan. Useat menetelmistä tarvitsevat ohjelmien ohjausvuokaavion tai vastaavaa informaatiota, jota voidaan laskea ohjelmien käännöksen yhteydessä. Tämän vuoksi menetelmät eivät ole yhteensopivia dynaamisesti tyypitettyjen tulkattujen ohjelmointikielten kanssa, joissa tätä informaatiota ei ole saatavilla. Tämä työ esittelee testien kattavuuteen perustuvan menetelmän regressiotestien valintaan, jota voidaan käyttää tulkattujen ohjelmointikielien kanssa. Esitelty menetelmä ei tarvitse muutoksia testattavan ohjelman ohjelmakoodiin. Esitellyn menetelmän testien valinnan tarkkuutta testattiin keskikokoisella verkkosovelluksella, ja tulokset olivat osittain ristiriitaisia. Testien valintamenetelmä onnistui valitsemaan pienen testijoukon noin puolessa testitilanteita. Lopuissa testitilanteista menetelmä joutui suorittamaan kaikki testijoukon testit. Menetelmän käyttämisen havaittiin kuitenkin hidastavan valittujen testien suoritusaikaa merkittävästi

Developoing A Computer and Network Engineering Major Curriculum For Vocational High School (VHS) in Indonesia

This study aims at developing curriculum for Computer and Network Engineering major which is relevant to industrial needs. The study employed the qualitative method. The data were collected through an in-depth interview, documentation, and focus group disscussion. The research population comprised of (1) industry practitioners from computer and network engineering industries, and (2) teachers of vocational high schools in Special Region of Yogyakarta. In this qualitative research, the one who became the instrument or tool of the research was the researcher himself. Understanding the qualitative research method and the knowledge related to the field of the research, the researcher was sure that he had sufficient knowledge both academically and technically. The findings of this study consisted of four parts, namely (1) standard competence of Computer and Network Engineering major for vocational high school; (2) the curriculum of Computer and Network Engineering major that is currently implemented; (3) competences in the field of Computer and Network Engineering demanded by industries; and (4) the curricuulum of Computer and Network Engineering major that is appropriate for industrial needs