Software testing

Summary

Software testing is the act of examining the artifacts and the behavior of the software under test by validation and verification. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but are not necessarily limited to:

analyzing the product requirements for completeness and correctness in various contexts like industry perspective, business perspective, feasibility and viability of implementation, usability, performance, security, infrastructure considerations, etc.
reviewing the product architecture and the overall design of the product
Working with product developers on improvement in coding techniques, design patterns, and tests that can be written as part of code based on various techniques like boundary conditions, etc.
executing a program or application with the intent of examining behavior
reviewing the deployment infrastructur

Official source

https://en.wikipedia.org/wiki/Software_testing

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Supporting Loop Proofs in KeY by using BLAST

Mathias Krebs

In contrast to beta-testing, formal verification can guarantee correctness of a program against a specification. Two basic verification techniques are theorem proving and model checking. Both have strengths and weaknesses. Theorem proving is powerful, but difficult to use for a software engineer. Model checking is fully automatic, but less powerful and hard to extend. This paper shows a possibility, how to combine both approaches, in order to surround the weaknesses. Concretely, we automatize the proof of while loops in the theorem prover KeY, by using the model checker BLAST.

2006

Efficient Testing of Recovery Code Using Fault Injection

George Candea, Paul Dan Marinescu

A critical part of developing a reliable software system is testing its recovery code. This code is traditionally difficult to test in the lab, and, in the field, it rarely gets to run; yet, when it does run, it must execute flawlessly in order to recover the system from failure. In this article, we present a library-level fault injection engine that enables the productive use of fault injection for software testing. We describe automated techniques for reliably identifying errors that applications may encounter when interacting with their environment, for automatically identifying high-value injection targets in program binaries, and for producing efficient injection test scenarios. We present a framework for writing precise triggers that inject desired faults, in the form of error return codes and corresponding side effects, at the boundary between applications and libraries. These techniques are embodied in LFI, a new fault injection engine we are distributing http://lfi.epfl.ch. This article includes a report of our initial experience using LFI. Most notably, LFI found 12 serious, previously unreported bugs in the MySQL database server, Git version control system, BIND name server, Pidgin IM client, and PBFT replication system with no developer assistance and no access to source code. LFI also increased recovery-code coverage from virtually zero up to 60% entirely automatically without requiring new tests or human involvement.

2011

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This paper makes the case for TaaS - automated software testing as a cloud-based service. We present three kinds of TaaS: (1) a public certification service, akin to Underwriters Labs, that independently assesses the reliability, safety, and security of software; (2) a "home edition" on-demand testing service for consumers to verify the programs they are about to install on their PC or mobile device; and (3) a "programmer's sidekick" enabling developers to thoroughly and promptly test their code with minimal upfront resource investment. Unlike existing test infrastructures, TaaS automatically tests software, without human involvement from the service customer's or provider's side. We argue that recent techniques for automated testing - even if usable only on toy programs - can become practical by harnessing the resources of compute clouds. TaaS can help reduce bug density in all software we depend on. Our work suggests the technical feasibility of doing so, and we further argue that it is compelling also from a social and business point of view.

2010