76,286 research outputs found
A Survey on Software Testing Techniques using Genetic Algorithm
The overall aim of the software industry is to ensure delivery of high
quality software to the end user. To ensure high quality software, it is
required to test software. Testing ensures that software meets user
specifications and requirements. However, the field of software testing has a
number of underlying issues like effective generation of test cases,
prioritisation of test cases etc which need to be tackled. These issues demand
on effort, time and cost of the testing. Different techniques and methodologies
have been proposed for taking care of these issues. Use of evolutionary
algorithms for automatic test generation has been an area of interest for many
researchers. Genetic Algorithm (GA) is one such form of evolutionary
algorithms. In this research paper, we present a survey of GA approach for
addressing the various issues encountered during software testing.Comment: 13 Page
Some security issues for web based frameworks
This report investigates whether a vulnerability found in one web framework may be used to find a vulnerability in a different web framework. To test this hypothesis, several open source applications were installed in a secure test environment together with security analysis tools. Each one of the applications were developed using a different software framework. The results show that a vulnerability identified in one framework can often be used to find similar vulnerabilities in other frameworks. Crosssite scripting security issues are the most likely to succeed when being applied to more than one framework
Semantics-based Automated Web Testing
We present TAO, a software testing tool performing automated test and oracle
generation based on a semantic approach. TAO entangles grammar-based test
generation with automated semantics evaluation using a denotational semantics
framework. We show how TAO can be incorporated with the Selenium automation
tool for automated web testing, and how TAO can be further extended to support
automated delta debugging, where a failing web test script can be
systematically reduced based on grammar-directed strategies. A real-life
parking website is adopted throughout the paper to demonstrate the effectivity
of our semantics-based web testing approach.Comment: In Proceedings WWV 2015, arXiv:1508.0338
Trustworthy Experimentation Under Telemetry Loss
Failure to accurately measure the outcomes of an experiment can lead to bias
and incorrect conclusions. Online controlled experiments (aka AB tests) are
increasingly being used to make decisions to improve websites as well as mobile
and desktop applications. We argue that loss of telemetry data (during upload
or post-processing) can skew the results of experiments, leading to loss of
statistical power and inaccurate or erroneous conclusions. By systematically
investigating the causes of telemetry loss, we argue that it is not practical
to entirely eliminate it. Consequently, experimentation systems need to be
robust to its effects. Furthermore, we note that it is nontrivial to measure
the absolute level of telemetry loss in an experimentation system. In this
paper, we take a top-down approach towards solving this problem. We motivate
the impact of loss qualitatively using experiments in real applications
deployed at scale, and formalize the problem by presenting a theoretical
breakdown of the bias introduced by loss. Based on this foundation, we present
a general framework for quantitatively evaluating the impact of telemetry loss,
and present two solutions to measure the absolute levels of loss. This
framework is used by well-known applications at Microsoft, with millions of
users and billions of sessions. These general principles can be adopted by any
application to improve the overall trustworthiness of experimentation and
data-driven decision making.Comment: Proceedings of the 27th ACM International Conference on Information
and Knowledge Management, October 201
- ā¦