Operational engineers can learn the benefits of AIOps, developers can learn about fuzzing and automated code reviews backed by AI and ML, test automation engineers can learn about autonomous testers and RPA, and more. Kinsbruner : DevOps and Agile have significantly matured over the past year, and there is a strong understanding around the value of fast software delivery and incremental value to customers.
With that in mind, the industry is still struggling with few major challenges, including:. More advanced tools that are based on AI and ML are growing and when used in parallel with standard ones, they can improve the overall efficiency, performance, and quality of software deliveries. InfoQ: What are the challenges that software teams are facing these days when it comes to quality and testing? Kinsbruner : There are multiple challenges that can be divided across test automation creation and maintenance, test reporting and analysis, test management, testing trends, and debugging.
Traditional tools are not efficient enough to provide practitioners with reliable, robust, and maintainable test scripts. Ongoing maintenance of scripts is also a challenge that causes lots of false negatives and noise that drills into the CI pipeline. As test execution scales, large test data accumulates and needs to be sliced and diced to find the most relevant issues.
Here, traditional tools are limited in filtering big test data and providing data-driven smart decisions, trends, root cause of failures, and more. Hence, AI and ML are in a great position to close this gap by automatically generating test code and maintaining it through self-healing methods.
Specifically, AIOps uses big data, analytics, and machine learning capabilities to do the following:. With the power of AI-based operations, teams can better focus on determining the service heath of their applications, and gain control and visibility over their production data. With that, DevOps teams can expedite their MTTR mean time to resolution using automated incident management in real time and quickly.
InfoQ: What solutions do artificial intelligence and machine learning provide for test automation? Kinsbruner : AI and ML based tools for test automation provide a wide range of abilities. From a creation point of view, AI and ML tools can generate test scenarios autonomously without writing a line of code. This can be done via NLP natural language processes and other methods. On other fronts, ML tools for test automation can utilize self-healing abilities to auto-maintain object locators for web and mobile apps in an agnostic way.
In such cases, ML can run through the test data, build acceptance test results on the code itself and provide predictive analysis, trends, and guidelines around which regression tests to run for the next build, what coverage gaps exist, and much more.
The main benefit here is to optimize the regression test suite to cover the most valuable test cases based on data, history, and predictive analysis.
Lastly, there are ML tools for the automated creation of unit tests that can ease the work for the developers and accelerate their development cycle and their build acceptance testing BAT.
The first item to investigate is the data accuracy itself. The next consideration is around the use of static vs. Obviously for dynamic ones, there is a greater need to examine and continuously test the datasets vs.
Complex systems would use more than a single NN, hence, the testing of each and the dependency on one process or the next will be a critical success factor e. Like in any other system, security is a key aspect to cover. For AI based systems, uncovering all the security-related flaws is essential.
Here, the test engineer would need to identify the use cases, the potential limitations of the AI algorithm that can allow users to trick the system through different inputs to the system etc. While there is more data-related testing to consider, the additional aspect to consider is the fitness of such testing types in the overall product, tool stack, pipeline, and tool selection.
Within the book there is a full methodology with examples on how to get started with testing such systems. Kinsbruner : The book covers a wide range of possibilities to test chatbots apps, mobile and web apps, and desktop or business apps. Through RPA tools, testers can automate inner processes for the business and reduce the time and cost of doing it manually. For test automation creation as mentioned above, AI can auto-generate and maintain the scripts upon code and environment changes.
For visual testing, AI can leverage neural networks to generate baselines and compare between different visuals across devices, web browsers and more automatically. Tarek K. Flagging a list will send it to the Goodreads Customer Care team for review. We take abuse seriously in our book lists. Only flag lists that clearly need our attention. As a general rule we do not censor any content on the site. The only content we will consider removing is spam, slanderous attacks on other members, or extremely offensive content eg.
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Weinberg 3. What Did You Say? Seashore 4. It is not easy to quantify quality. It is, therefore, often assessed in terms of its characteristics. Ghezzi et al. Furthermore quality is also determined by the process employed to design the product, product characteristics and the project attributes in terms of life cycle stages. Every application area also has its own specialized quality requirements which need to be considered while designing quality objectives of a software project.
This highlights the fact that there is no strict definition of quality as each software development project and product has its own quality goals. In this chapter, we highlight how the quality can be improved in each software lifecycle phase and also suggest methodologies that can be put in place to improve the software development projects. Offer does not apply to e-Collections and exclusions of select titles may apply. Offer expires June 30, Browse Titles.
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