scholarly journals Quantum software testing: State of the art

2021 ◽  
Author(s):  
Antonio García de la Barrera ◽  
Ignacio García‐Rodríguez de Guzmán ◽  
Macario Polo ◽  
Mario Piattini
Keyword(s):  
Software Testing ◽  
State Of The Art

scholarly journals First international competition on software testing

2021 ◽  
Author(s):  
Dirk Beyer
Keyword(s):  
Software Testing ◽  
Test Generation ◽  
Comparative Evaluation ◽  
State Of The Art ◽  
International Competition ◽  
Software Test ◽  
Test Specifications ◽  
Research Areas ◽  
Function Call ◽  
Generation Test

AbstractTool competitions are a special form of comparative evaluation, where each tool has a team of developers or supporters associated that makes sure the tool is properly configured to show its best possible performance. In several research areas, tool competitions have been a driving force for the development of mature tools that represent the state of the art in their field. This paper describes and reports the results of the 1$$^{\text {st}}$$ st International Competition on Software Testing (Test-Comp 2019), a comparative evaluation of automatic tools for software test generation. Test-Comp 2019 was presented as part of TOOLympics 2019, a satellite event of the conference TACAS. Nine test generators were evaluated on 2 356 test-generation tasks. There were two test specifications, one for generating a test that covers a particular function call and one for generating a test suite that tries to cover the branches of the program.

Genetic Algorithms in Test Design Automation

2014 ◽  
Vol 693 ◽  
pp. 153-158 ◽  
Author(s):  
Michal Sroka ◽  
Roman Nagy ◽  
Dominik Fisch
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Design Automation ◽  
State Of The Art ◽  
Embedded Software ◽  
Test Design ◽  
Test Case ◽  
Current State ◽  
Development Costs ◽  
Case Design

Automation in the software testing process has significant impact on the overall software development in industry. Therefore, any automation in software testing has huge influence on overall development costs. The present article reviews the current state of the art of test case design automation via genetic algorithms. Three approaches applied in software testing are described with regards to their applicability in the testing of embedded software.

scholarly journals Software Testing for Machine Learning

2020 ◽  
Vol 34 (09) ◽  
pp. 13576-13582
Author(s):  
Dusica Marijan ◽  
Arnaud Gotlieb
Keyword(s):  
Machine Learning ◽  
Software Testing ◽  
Research Agenda ◽  
Software Verification ◽  
State Of The Art ◽  
Learning Systems ◽  
Safety Critical ◽  
Current State ◽  
Established Technique ◽  
Summary Talk

Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance calls into question the widespread use of machine learning, especially in safety-critical applications, unless we are able to assure its correctness and trustworthiness properties. Software verification and testing are established technique for assuring such properties, for example by detecting errors. However, software testing challenges for machine learning are vast and profuse - yet critical to address. This summary talk discusses the current state-of-the-art of software testing for machine learning. More specifically, it discusses six key challenge areas for software testing of machine learning systems, examines current approaches to these challenges and highlights their limitations. The paper provides a research agenda with elaborated directions for making progress toward advancing the state-of-the-art on testing of machine learning.

scholarly journals A Pattern-Based Software Testing Framework for Exploitability Evaluation of Metadata Corruption Vulnerabilities

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Fenglei Deng ◽  
Jian Wang ◽  
Bin Zhang ◽  
Chao Feng ◽  
Zhiyuan Jiang ◽  
...  
Keyword(s):  
Software Testing ◽  
Software Quality ◽  
Intermediate State ◽  
State Of The Art ◽  
Software Quality Assurance ◽  
Human Knowledge ◽  
Layout Problem ◽  
Testing Framework ◽  
Automated Tools ◽  
Procedural Method

In recent years, increased attention is being given to software quality assurance and protection. With considerable verification and protection schemes proposed and deployed, today’s software unfortunately still fails to be protected from cyberattacks, especially in the presence of insecure organization of heap metadata. In this paper, we aim to explore whether heap metadata could be corrupted and exploited by cyberattackers, in an attempt to assess the exploitability of vulnerabilities and ensure software quality. To this end, we propose RELAY, a software testing framework to simulate human exploitation behavior for metadata corruption at the machine level. RELAY employs the heap layout serialization method to construct exploit patterns from human expertise and decomposes complex exploit-solving problems into a series of intermediate state-solving subproblems. With the heap layout procedural method, RELAY makes use of the fewer resources consumed to solve a layout problem according to the exploit pattern, activates the intermediate state, and generates the final exploit. Additionally, RELAY can be easily extended and can continuously assimilate human knowledge to enhance its ability for exploitability evaluation. Using 20 CTF&RHG programs, we then demonstrate that RELAY has the ability to evaluate the exploitability of metadata corruption vulnerabilities and works more efficiently compared with other state-of-the-art automated tools.

scholarly journals Software Testing or The Bugs’ Nightmare

2021 ◽  
pp. 1-21
Author(s):  
Hector David Menendez
Keyword(s):  
Software Testing ◽  
Test Generation ◽  
State Of The Art ◽  
Symbolic Execution ◽  
Significant Progress ◽  
Automatic Test Generation ◽  
Significant Development ◽  
Testing Strategies ◽  
Future Challenges ◽  
Single Part

Software development is not error-free. For decades, bugs –including physical ones– have become a significant development problem requiring major maintenance efforts. Even in some cases, solving bugs led to increment them. One of the main reasons for bug’s prominence is their ability to hide. Finding them is difficult and costly in terms of time and resources. However, software testing made significant progress identifying them by using different strategies that combine knowledge from every single part of the program. This paper humbly reviews some different approaches from software testing that discover bugs automatically and presents some different state-of-the-art methods and tools currently used in this area. It covers three testing strategies: search-based methods, symbolic execution, and fuzzers. It also provides some income about the application of diversity in these areas, and common and future challenges on automatic test generation that still need to be addressed.

State-of-the-Art Software Testing

IEEE Software ◽  
2017 ◽  
Vol 34 (5) ◽  
pp. 4-6 ◽  
Author(s):  
Diomidis Spinellis
Keyword(s):  
Software Testing ◽  
State Of The Art

Practical Picture Processing

1974 ◽  
Vol 32 ◽  
pp. 338-339
Author(s):  
T. A. Welton
Keyword(s):  
Radiation Damage ◽  
Coherence Length ◽  
Spatial Information ◽  
State Of The Art ◽  
Coherent Radiation ◽  
The State ◽  
Energy Spread ◽  
Electron Micrograph ◽  
Picture Processing ◽  
Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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