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Regression testing repeatedly executes test cases of previous builds to validate that the original features are not affected with any new changes. In recent years, regression testing has seen a remarkable progress with the increasing popularity of agile methods, which stress the central role of regression testing in maintaining software quality. The optimum case for regression testing in agile context is to run regression set at the end of each sprint and release, which requires a lot of cost and time. In this paper, we present an automated agile regression testing approach on both the sprints and release levels. The proposed approach addresses both weighted sprint test cases prioritization technique, which prioritizes test cases based on several parameters having real practical weight for testers, and Cluster‐based Release Test cases Selection technique that clusters user stories based on the similarity of covered modules to solve the scalability issue. Test cases are then selected based on issues logged for failed test cases using text mining techniques. The proposed approach achieves enhancement for both the prioritization and selection of test cases for agile regression testing. Copyright © 2016 John Wiley & Sons, Ltd.

Test case generation is a core phase in any testing process, therefore automating it plays a tremendous role in reducing the time and effort spent during the testing process.This paper proposes an enhanced XML-based automated approach for generating test cases from activity diagrams. The proposed architecture creates a special table called Activity Dependency Table (ADT) for each XML file. The ADT covers all the functionalities in the activity diagram as well as handling the decisions, loops, fork, join, merge, object and conditional threads.Then it automatically generates a directed graph called Activity Dependency Graph (ADG) that is used in conjunction with the ADT to extract all the possible final test cases. The proposed model validates the generated test paths during the generation process to ensure meeting a hybrid coverage criterion. The generated test cases can be sent to any requirements management tool to be traced against the requirements.The proposed model is prototyped on 30 differently sized activity diagrams in different domains An experimental evaluation of the proposed model is done as well. It saves time and effort besides, increases the quality of generated test cases, therefore optimizes the overall performance of the testing process Moreover, the generated test cases can be executed on the system under test using any automatic test execution tool.

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Tremendous systems are rapidly evolving based on the trendy Internet of Things (IoT) in various domains. Different technologies are used for communication between the massive connected devices through all layers of the IoT system, causing many security and performance issues. Regression and integration testing are considered repeatedly, in which the vast costs and efforts associated with the frequent execution of these inflated test suites hinder the adequate testing of such systems. This necessitates the focus on exploring innovative scalable testing approaches for large test suites in IoT-based systems. In this paper, a scalable framework for continuous integration and regression testing in IoT-based systems (IoT-CIRTF) is proposed, based on IoT-related criteria for test case prioritization and selection. The framework utilizes search-based techniques to provide an optimized prioritized set of test cases to select from. The selection is based on a trained prediction model for IoT standard components using supervised deep learning algorithms to continuously ensure the overall reliability of IoT-based systems. The experiments are held on two GSM datasets. The experimental results achieved prioritization accuracy up to 90% and 92% for regression testing and integration testing respectively. This provides an enhanced and efficient framework for continuous testing of IoT-based systems, as per IoT-related criteria for the prioritization and selection purposes.

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Distributed applications are notoriously difficult to develop and manage due to their inherent dynamics and heterogeneity of component technologies and network protocols. Middleware technologies dramatically simplify the development of distributed applications, but they still prove difficult to manage at runtime. This paper considers the "on-going" development of a framework that provides instrumentation and control services, which extend core middleware services, to realize the runtime management and adaptation of distributed applications. The instrumentation and control services are used in conjunction with dependency management utilities to measure performance, monitor behaviour and resolve the runtime inconsistencies and conflicts that may occur in distributed applications.

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