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Stress testing is a software testing activity that determines the robustness of software by testing beyond the limits of normal operation. Stress testing is particularly important for "mission critical" software, but is used for all types of software. Stress tests commonly put a greater emphasis on robustness, availability, and error handling under a heavy load, than on what would be considered correct behavior under normal circumstances.
A system stress test refers to tests that put a greater emphasis on robustness, availability, and error handling under a heavy load, rather than on what would be considered correct behavior under normal circumstances. In particular, the goals of such tests may be to ensure the software does not crash in conditions of insufficient computational resources (such as memoryordisk space), unusually high concurrency, or denial of service attacks.
Examples:
Stress testing may be contrasted with load testing:
Failures may be related to:
Reasons for stress testing include:
Branch coverage (a specific type of code coverage) is a metric of the number of branches executed under test, where "100% branch coverage" means that every branch in a program has been executed at least once under some test. Branch coverage is one of the most important metrics for software testing; software for which the branch coverage is low is not generally considered to be thoroughly tested. Note that[editorializing] code coverage metrics are a property of the tests for a piece of software, not of the software being tested.
Achieving high branch coverage often involves writing negative test variations, that is, variations where the software is supposed to fail in some way, in addition to the usual positive test variations, which test intended usage. An example of a negative variation would be calling a function with illegal parameters. There is a limit to the branch coverage that can be achieved even with negative variations, however, as some branches may only be used for handling of errors that are beyond the control of the test. For example, a test would normally have no control over memory allocation, so branches that handle an "out of memory" error are difficult to test.
Stress testing can achieve higher branch coverage by producing the conditions under which certain error handling branches are followed. The coverage can be further improved by using fault injection.
Stress testing usually consists of testing beyond specified limits in order to determine failure points and test failure recovery.[1][2]
Load testing implies a controlled environment moving from low loads to high. Stress testing focuses on more random events, chaos and unpredictability. Using a web application as an example here are ways stress might be introduced:[1]
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A Pattern-Based Software Testing Framework for Exploitability Evaluation of Metadata Corruption Vulnerabilities developed by Deng Fenglei, Wang Jian, Zhang Bin, Feng Chao, Jiang Zhiyuan, Su Yunfei discuss how there is increased attention in software quality assurance and protection. However, today’s software still unfortunately fails to be protected from cyberattacks, especially in the presence of insecure organization of heap metadata. The authors aim to explore whether heap metadata could be corrupted and exploited by cyber-attackers, and they propose RELAY, a software testing framework to simulate human exploitation behavior for metadata corruption at the machine level. RELAY also makes use of the fewer resources consumed to solve a layout problem according to the exploit pattern, and generates the final exploit.
A Methodology to Define Learning Objects Granularity developed by BENITTI, Fabiane Barreto Vavassori. The authors first discuss how learning object is one of the main research topics in the e-learning community in recent years and granularity is a key factor for learning object reuse. The authors then present a methodology to define the learning objects granularity in the computing area as well as a case study in software testing. Later, the authors carry out five experiments to evaluate the learning potential from the produced learning objects, as well as to demonstrate the possibility of learning object reuse. Results from the experiment are also presented in the article, which show that learning object promotes the understanding and application of the concepts.
A recent article, Reliability Verification of Software Based on Cloud Service, have a ground breaking effect and it explores how software industry needs a way to measure reliability of each component of the software. In this article, a guarantee-verification method based on cloud service was proposed. The article first discusses how trustworthy each component's are will be defined in terms of component service guarantee-verification. Then an effective component model was defined in the article and based on the proposed model, the process of verifying a component service is illustrated in an application sample.
