A systematic review on regression test selection techniques

Regression testing is verifying that previously functioning software remains after a change. With the goal of finding a basis for further research in a joint industry-academia research project, we conducted a systematic review of empirical evaluations of regression test selection techniques. We identified 27 papers reporting 36 empirical studies, 21 experiments and 15 case studies. In total 28 techniques for regression test selection are evaluated. We present a qualitative analysis of the findings, an overview of techniques for regression test selection and related empirical evidence. No technique was found clearly superior since the results depend on many varying factors. We identified a need for empirical studies where concepts are evaluated rather than small variations in technical implementations

Towards a Regression Test Selection Technique for Message-Based Software Integration

Regression testing is essential to ensure software quality. Regression Test-case selection is another process wherein, the testers would like to ensure that test-cases which are obsolete due to the changes in the system should not be considered for further testing. This is the Regression Test-case Selection problem. Although existing research has addressed many related problems, most of the existing regression test-case selection techniques cater to procedural systems. Being academic, they lack the scalability and detail to cater to multi-tier applications. Such techniques can be employed for procedural systems, usually mathematical applications. Enterprise applications have become complex and distributed leading to component-based architectures. Thus, inter-process communication has become a very important activity of any such system. Messaging is the most widely employed intermodule interaction mechanism. Today\u27s systems, being heavily internet dependent, are Web-Services based which utilize XML for messaging. We propose an RTS technique which is specifically targeted at enterprise applications

Towards a Regression Test Selection Technique for Message-Based Software Integration

Regression testing is essential to ensure software quality. Regression Test-case selection is another process wherein, the testers would like to ensure that test-cases which are obsolete due to the changes in the system should not be considered for further testing. This is the Regression Test-case Selection problem. Although existing research has addressed many related problems, most of the existing regression test-case selection techniques cater to procedural systems. Being academic, they lack the scalability and detail to cater to multi-tier applications. Such techniques can be employed for procedural systems, usually mathematical applications. Enterprise applications have become complex and distributed leading to component-based architectures. Thus, inter-process communication has become a very important activity of any such system. Messaging is the most widely employed intermodule interaction mechanism. Today\u27s systems, being heavily internet dependent, are Web-Services based which utilize XML for messaging. We propose an RTS technique which is specifically targeted at enterprise applications

Exploring regression testing and software product line testing - research and state of practice

In large software organizations with a product line development approach a selective testing of product variants is necessary in order to keep pace with the decreased development time for new products, enabled by the systematic reuse. The close relationship between products in product line indicates an option to reduce the testing effort due to redundancy. In many cases test selection is performed manually, based on test leaders’ expertise. This makes the cost and quality of the testing highly dependent on the skills and experience of the test leaders. There is a need in industry for systematic approaches to test selection. The goal of our research is to improve the control of the testing and reduce the amount of redundant testing in the product line context by applying regression test selection strategies. In this thesis, the state of art of regression testing and software product line testing are explored. Two extensive systematic reviews are conducted as well as an industrial survey of regression testing state of practice and an industrial evaluation of a pragmatic regression test selection strategy. Regression testing is not an isolated one-off activity, but rather an activity of varying scope and preconditions, strongly dependent on the context in which it is applied. Several techniques for regression test selection are proposed and evaluated empirically but in many cases the context is too specific for a technique to be easily applied directly by software developers. In order to improve the possibility for generalizing empirical results on regression test selection, guidelines for reporting the testing context are discussed in this thesis. Software product line testing is a relatively new research area. The understanding about challenges is well established but when looking for solutions to these challenges, we mostly find proposals, and empirical evaluations are sparse. Regression test selection strategies proposed in literature are not easily applicable in the product line context. Instead, control may be increased by increased visibility of the effects of testing and proper measurements of software quality. Focus of our future work will be on how to guide the planning and assessment of regression testing activities in large, complex reuse based systems, by visualizing the quality achieved in different parts of the system and evaluating the effects of different selection strategies when applied in various regression testing situations

An Infrastructure to Support Interoperability in Reverse Engineering

An infrastructure that supports interoperability among reverse engineering tools and other software tools is described. The three major components of the infrastructure are: (1) a hierarchy of schemas for low- and middle-level program representation graphs, (2) g4re, a tool chain for reverse engineering C++ programs, and (3) a repository of reverse engineering artifacts, including the previous two components, a test suite, and tools, GXL instances, and XSLT transformations for graphs at each level of the hierarchy. The results of two case studies that investigated the space and time costs incurred by the infrastructure are provided. The results of two empirical evaluations that were performed using the api module of g4re, and were focused on computation of object-oriented metrics and three-dimensional visualization of class template diagrams, respectively, are also provided

DDoS: DeepDefence and Machine Learning for identifying attacks

Distributed Denial of Service (DDoS) attacks are very common type of computer attack in the world of internet today. Automatically detecting such type of DDoS attack packets & dropping them before passing through the network is the best prevention method. Conventional solution only monitors and provide the feedforward solution instead of the feedback machine-based learning. A Design of Deep neural network has been suggested in this work and developments have been made on proactive detection of attacks. In this approach, high level features are extracted for representation and inference of the dataset. Experiment has been conducted based on the ISCX dataset published in year 2017,2018 and CICDDoS2019 and program has been developed in Matlab R17b, utilizing Wireshark for features extraction from the datasets. Network Intrusion attacks on critical oil and gas industrial installation become common nowadays, which in turn bring down the giant industrial sites to standstill and suffer financial impacts. This has made the production companies to started investing millions of dollars revenue to protect their critical infrastructure with such attacks with the active and passive solutions available. Our thesis constitutes a contribution to such domain, focusing mainly on security of industrial network, impersonation and attacking with DDoS

Custom Windows Patching Methodology - Comparative Analysis

Windows Server Update Services has been a common mainstay among organizations with a heavy footprint of Windows operating systems since it was originally released as Software Update Services in 2002. While the product has grown in scope, the primary allure remains the same: WSUS offers organizations greater control over the patches that are released to their environment and saves bandwidth by allowing a centralized device to download and offer patches to internal clients rather than having each of those clients download the content they require from the Internet. Unfortunately, the product has a structural limitation in that it lacks the capacity to provide high-availability to the metadata synchronization process that must occur in order to deliver the most up-to-date patches to endpoints. WSUS metadata contains details about the individual updates, EULAs, and supersedence relationships. Due to design limitations and the growing concern of outages, a solution was developed to supplement and perhaps replace WSUS in certain scenarios. This solution, dubbed the Custom Patching Manager (CPM), is an extension of a concept originally started by Alejandro Gómez Galindo and finds middle-ground between Windows Server Update Services and Windows Update using freely available software. The solution assesses the vulnerabilities of a system or systems, determines whether or not the patches are part of an approved list, determines whether or not the content for missing updates is available locally, acquires that content depending on the previous step, and applies the patches to the endpoint. This proof-of-concept proved functional and reliable but would benefit from some optimizations that have been recommended as future works