Search-Based Software Testing for Formal Software Verification – and Vice Versa

2020 ◽  
pp. 3-6
Author(s):  
Shiva Nejati
Keyword(s):  
Software Testing ◽  
Software Verification ◽  
Formal Software Verification

scholarly journals A comparison of tools for teaching formal software verification

2008 ◽  
Vol 21 (3) ◽  
pp. 293-301 ◽  
Author(s):  
Ingo Feinerer ◽  
Gernot Salzer
Keyword(s):  
Software Verification ◽  
Formal Software Verification

scholarly journals A Survey of Automated Techniques for Formal Software Verification

2008 ◽  
Vol 27 (7) ◽  
pp. 1165-1178 ◽  
Author(s):  
Vijay D'Silva ◽  
Daniel Kroening ◽  
Georg Weissenbacher
Keyword(s):  
Software Verification ◽  
Formal Software Verification

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 Formal software verification measures up

2021 ◽  
Vol 64 (7) ◽  
pp. 13-15
Author(s):  
Samuel Greengard
Keyword(s):  
Software Verification ◽  
Mathematical Proofs ◽  
Formal Software Verification

Verified coding techniques use mathematical proofs to ensure code is error-free and hacker-resistant. Can the approach revolutionize software?

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