Software Testing Methodologies: A Information Review

In today’s Complex era, the need for simplest software application has increased massively. The quality of such a  handy application along with adequate testing is the biggest challenge one can face. Software Testing is an integral part of any software development which has to be followed right from the sapling phase of development. This paper focuses on testing methodologies which are used prior along with testing techniques for quality assurance and best of the quality

Automatic instantiation of abstract tests on specific configurations for large critical control systems

Computer-based control systems have grown in size, complexity, distribution and criticality. In this paper a methodology is presented to perform an abstract testing of such large control systems in an efficient way: an abstract test is specified directly from system functional requirements and has to be instantiated in more test runs to cover a specific configuration, comprising any number of control entities (sensors, actuators and logic processes). Such a process is usually performed by hand for each installation of the control system, requiring a considerable time effort and being an error prone verification activity. To automate a safe passage from abstract tests, related to the so called generic software application, to any specific installation, an algorithm is provided, starting from a reference architecture and a state-based behavioural model of the control software. The presented approach has been applied to a railway interlocking system, demonstrating its feasibility and effectiveness in several years of testing experience

Regression testing framework for test cases generation and prioritization

A regression test is a significant part of software testing. It is used to find the maximum number of faults in software applications. Test Case Prioritization (TCP) is an approach to prioritize and schedule test cases. It is used to detect faults in the earlier stage of testing environment. Code coverage is one of the features of a Regression Test (RT) that detects more number of faults from a software application. However, code coverage and fault detection are reducing the performance of existing test case prioritization by consuming a lot of time for scanning an entire code. The process of generating test cases plays an important role in the prioritization of test cases. The existing automated generation and prioritization techniques produces insufficient test cases that cause less fault detection rate or consumes more computation time to detect more faults. Unified Modelling Language (UML) based test case generation techniques can extract test cases from UML diagrams by covering maximum part of a module of an application. Therefore, a UML based test case generation can support a test case prioritization technique to find a greater number of faults with shorter execution time. A multi-objective optimization technique able to handle multiple objectives that supports RT to generate more number of test cases as well as increase fault detection rate and produce a better result. The aim of this research is to develop a framework to detect maximum number of faults with less execution time for improving the RT. The performance of the RT can be improved by an efficient test case generation and prioritization method based on a multi-objective optimization technique by handling both test cases and rate of fault detection. This framework consists of two important models: Test Case Generation (TCG) and TCP. The TCG model requires an UML use case diagram to extract test cases. A meta heuristic approach is employed that uses tokens for generating test cases. And, TCP receives the extracted test cases with faults as input to produce the prioritized set of test cases. The proposed research has modified the existing Hill Climbing based TCP by altering its test case swapping feature and detect faults in a reasonable execution time. The proposed framework intends to improve the performance of regression testing by generating and prioritizing test cases in order to find a greater number of faults in an application. Two case studies are conducted in the research in order to gather Test Case (TC) and faults for multiple modules. The proposed framework yielded a 92.2% of Average Percentage Fault Detection with less amount of testing time comparing to the other artificial intelligence-based TCP. The findings were proved that the proposed framework produced a sufficient amount of TC and found the maximum number of faults in less amount of time

Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods

Automated decisions are increasingly part of everyday life, but how can the public scrutinize, understand, and govern them? To begin to explore this, Omidyar Network has, in partnership with Upturn, published Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods.The report is based on an extensive review of computer and social science literature, a broad array of real-world attempts to study automated systems, and dozens of conversations with global digital rights advocates, regulators, technologists, and industry representatives. It maps out the landscape of public scrutiny of automated decision-making, both in terms of what civil society was or was not doing in this nascent sector and what laws and regulations were or were not in place to help regulate it.Our aim in exploring this is three-fold:1) We hope it will help civil society actors consider how much they have to gain in empowering the public to effectively scrutinize, understand, and help govern automated decisions; 2) We think it can start laying a policy framework for this governance, adding to the growing literature on the social and economic impact of such decisions; and3) We're optimistic that the report's findings and analysis will inform other funders' decisions in this important and growing field

Fail-Safe Testing of Safety-Critical Systems

This dissertation proposes an approach for testing of safety-critical systems. It is based on a behavioral and a fault model. The two models are analyzed for compatibility and necessary changes are identified to make them compatible. Then transformation rules are used to transform the fault model into the same model type as the behavioral model. Integration rules define how to combine them. This approach results in an integrated model which then can be used to generate tests using a variety of testing criteria. The dissertation illustrates this general framework using a CEFSM for the behavioral model and a Fault Tree for the fault model. We apply the technique to a variety of applications such as a Gas burner, an Aerospace Launch System, and a Railroad Crossing Control System. We also investigate the scalability of the approach and compare its efficiency with integrating a state chart and a fault tree. Construction and Analysis of Distributed Processes (CADP) has been used as a supporting tool for this approach to generate test cases from the integrated model and to analyze the integrated model for some properties such as deadlock and livelock