software testing


Machine Learning Lends a Hand for Automated Software Testing - The New Stack

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Now, a set of artificial intelligence-powered options like Microsoft's Security Risk Detection service and Diffblue's security scanner and test generation tools aim to make these techniques easier, faster and accessible to more developers. Microsoft Security Risk Detection (previously known as Project Springfield) takes a slightly different approach. The AI in Springfield combines two techniques; time travel debugging and constraint solving. Molnar is the researcher running the team behind Springfield; previously he helped apply the same techniques to products like Windows and Microsoft Office, finding a third of the security bugs discovered by fuzzing in the Windows 7 client.


Artificial Intelligence, Communication, and the Evolution of Software Testing - DZone AI

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A highly regarded speaker in the conference circuit and luminary in the software testing world, she approaches the challenges of quality assurance with deep insight. All of that has come together into my main interest at the moment: The UX and usability of testing tools for testers. Isabel: In the 70s or 80s, someone famously wrote, "Don't talk about computer interfaces; all interfaces are human interfaces." At the same time, development has gone from small focused teams working on a specific problem through to big projects with silo working and now coming back to people saying they need more frequent deliveries -- essentially, the rise of Agile and DevOps.


Bots and AI: The Future of Software Testing and Development - DZone AI

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Bots and AI have affected software testing and development in terms of testing scope and workloads, debugging adequacy, and advanced continuous testing. Software testers can have a full team of robotic test automation running a wide scope of tests and make it their task to oversee, examine, and assist them in programming the testing procedure. Utilizing artificial intelligence in robotics to advance continuous testing can expand the extent of ongoing testing capacities. They may not exactly be here yet, but the use of artificial intelligence in software testing quality and reliability is coming very soon.


How do I tackle machine learning in software testing?

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Welcome to the world of machine learning in software testing. Machine learning in software testing requires an entirely different approach. Testing these systems requires a deep understanding of the problem domain and the ability to quantify the results you need in that domain. For machine learning in software testing, you should also have a high-level understanding of the learning architecture.


How Machine Learning is Impacting the Way We Test Software

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Essentially, machine learning refers to computers or software platforms "learning" over a period of time. Other examples of machine learning include Google's self-driving car and the way websites deliver targeted ads to customers. For computer developers, machine learning has evolved considerably. Nothing is perfect, and modern machine learning systems and processes are no exception.


Performance Testing Guide @DevOpsSummit #DevOps #DX #APM #Monitoring

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Types of performance testing for software To understand how software will perform on users' systems, there different types of performance tests that can be applied during software testing. Spike testing Spike testing is a type of stress testing that evaluates software performance when workloads are substantially increased quickly and repeatedly. Volume testing Volume testing determines how efficiently software performs with a large, projected amounts of data. What Performance Testing Metrics are Measured Metrics are needed to understand the quality and effectiveness of performance testing.


Melbourne startup Bugdojo launches bot-powered QA tool ZDNet

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A lot of startups and mid-size development teams skip through quality assurance (QA) and software testing in favour of shipping new features and releases as quickly as possible, according to Australian startup Bugdojo. This is because QA and software testing is seen as time-consuming and expensive, the startup said. Founded by Melbourne entrepreneur Ash Conway, Bugdojo wanted to address these inconveniences by creating a QA tool that provides development teams access to software testers on-demand by using bot commands as they're building code. Nick Drewe, developer relations manager at Bugdojo, told ZDNet that while it's possible for companies to hire offshore testers cost-effectively, the testing is "far from constant". "Traditionally, QA resources are most needed towards the end of a release cycle, and are often a bottleneck in the process, requiring a huge backlog of testing to be cleared before release," Drewe said.


ATAGTR2017 Artificial Intelligence in Software Testing – Demystified

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Varies on the SDLC methodology followed – 20% 10% of the Defects arise the need of a requirement change Sanity check before being utilized. Agile Testing Alliance Global Testing Retreat 2017 MACHINE LEARNING NATURAL LANGUAGE PROCESSING AUTONOMOUS FRAMEWORKS CONTAINERIZATION & DEPLOYMENT AUTOMATION EMERGENCE OF LIFECYCLE AUTOMATION Have we looked at the factors contributing to leakage, optimal test coverage? How can we Automate the automation process and reduce hand-offs Artifact driven, baseline and auto compare, self heal capabilities, scriptless approach How can test teams help increase velocity, partner with Ops for validation, certification Integrated harmonized approach with tools and frameworks, frictionless E2E automation ..That are Mature and Assure a'Defect-free' Eco-system 11. Agile Testing Alliance Global Testing Retreat 2017 1. Autonomous Automation is Intelligent and Adaptable to Change Trigger/ Scheduler AI SystemDeveloper • Code refactoring or bug check-in • Correlates this information to tests suite and identify the impacted test cases • Computes changes done, affected files & sub systems • Run selected tests Interactive collection of autonomic elements Self govern and high level of adaptability to change Intelligence Increase predictability through self learning Improve quality through analytics & robotic automation Perform tests based on business rules Overall IT cost reduction Enables companies to use their IT assets in a more strategic manner Key Benefits 12. Agile Testing Alliance Global Testing Retreat 2017 Self Managing Self Configuration Self Optimization Self Protection Self Healing …with the Ability to Self Act, Regulate and Learn React to varying & unpredictable conditions in operating environment Self-Configuring, Self- Healing, Self-Optimizing and Self-Protecting Detect and to optimize suboptimal behaviors Detect problems and/or failures and to recover from them Protect itself from both external and internal factors 13. Agile Testing Alliance Global Testing Retreat 2017 2. Cognitive Automation Emulates Human Execution for Repetitive Processes Ease and accuracy in gathering and organizing data Aids advanced analytics Accuracy in prediction High performance Enables efficiency improvement Time, Cost and People reduction Key Benefits 14. Agile Testing Alliance Global Testing Retreat 2017 Through Natural Language Processing and Machine Learning HIGHLIGHTS Autonomous generation of scripts Better maintainability and ease of script modification Time-stamped selenium scripts – for versioning Reduction in tables of test data for the application under test Application Under Test To re-organize objects NLP Engine (Knowledge DB) Parse URL to pull objects Object mapped & Selenium Test Cases in Excel / XML / UML Automation Script Creation AI Agent Parse Test Cases 15.


Is artificial intelligence in software testing coming to you?

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But will there ever be a role for artificial intelligence in software testing, aka a machine software tester? Once that's possible, we combine machine learning about fields -- for example, fields named "first_name" have this common set of valid inputs: John, Michelle, Sarah, Robert -- with the model-driven techniques to take random walks through an application -- and we can have an army of machines with inductive expertise testing our software overnight. Most visual test tools allow their users to train the software to ignore fields that change all the time -- automatically generated date fields -- or to only focus on things that shouldn't change. Even with a machine software tester, visual inspection tools still need a human to run them.