With advancements in AI, particularly in machine learning and natural language processing, testing tools have become more sophisticated. AI-driven tools can now learn from previous tests, predict potential defects, and adapt to new testing environments with minimal human intervention. Typemock has been at the forefront of this evolution, continuously innovating to incorporate AI into its testing solutions.

RELATED: Addressing AI bias in AI-driven software testing

Typemock’s AI Enhancements

Typemock has developed AI-driven tools that significantly enhance efficiency, accuracy, and test coverage. By leveraging machine learning algorithms, these tools can automatically generate test cases, optimize testing processes, and identify potential issues before they become critical problems. This not only saves time but also ensures a higher level of software quality.

I believe AI in testing is not just about automation; it’s about intelligent automation. We harness the power of AI to enhance, not replace, the expertise of unit testers. 

Difference Between Automated Testing and AI-Driven Testing

Automated testing involves tools that execute pre-written test scripts automatically without human intervention during the test execution phase. These tools are designed to perform repetitive tasks, check for expected outcomes, and report any deviations. Automated testing improves efficiency but relies on pre-written tests.

AI-driven testing, on the other hand, involves the use of AI technologies to both create and execute tests. AI can analyze code, learn from previous test cases, generate new test scenarios, and adapt to changes in the application. This approach not only automates the execution but also the creation and optimization of tests, making the process more dynamic and intelligent.

While AI has the capability to generate numerous tests, many of these can be duplicates or unnecessary. With the right tooling, AI-driven testing tools can create only the essential tests and execute only those that need to be run. The danger of indiscriminately generating and running tests lies in the potential to create many redundant tests, which can waste time and resources. Typemock’s AI tools are designed to optimize test generation, ensuring efficiency and relevance in the testing process.

While traditional automated testing tools run predefined tests, AI-driven testing tools go a step further by authoring those tests, continuously learning and adapting to provide more comprehensive and effective testing.

Addressing AI Bias in Testing

AI bias occurs when an AI system produces prejudiced results due to erroneous assumptions in the machine learning process. This can lead to unfair and inaccurate testing outcomes, which is a significant concern in software development. 

To ensure that AI-driven testing tools generate accurate and relevant tests, it is essential to utilize the right tools that can detect and mitigate bias:

• Code Coverage Analysis: Use code coverage tools to verify that AI-generated tests cover all necessary parts of the codebase. This helps identify any areas that may be under-tested or over-tested due to bias.
• Bias Detection Tools: Implement specialized tools designed to detect bias in AI models. These tools can analyze the patterns in test generation and identify any biases that could lead to the creation of incorrect tests.
• Feedback and Monitoring Systems: Establish systems that allow continuous monitoring and feedback on the AI’s performance in generating tests. This helps in early detection of any biased behavior.

Ensuring that the tests generated by AI are effective and accurate is crucial. Here are methods to validate the AI-generated tests:

• Test Validation Frameworks: Use frameworks that can automatically validate the AI-generated tests against known correct outcomes. These frameworks help ensure that the tests are not only syntactically correct but also logically valid.
• Error Injection Testing: Introduce controlled errors into the system and verify that the AI-generated tests can detect these errors. This helps ensure the robustness and accuracy of the tests.
• Manual Spot Checks: Conduct random spot checks on a subset of the AI-generated tests to manually verify their accuracy and relevance. This helps catch any potential issues that automated tools might miss.

How Can Humans Review Thousands of Tests They Didn’t Write?

Reviewing a large number of AI-generated tests can be daunting for human testers, making it feel similar to working with legacy code. Here are strategies to manage this process:

• Clustering and Prioritization: Use AI tools to cluster similar tests together and prioritize them based on risk or importance. This helps testers focus on the most critical tests first, making the review process more manageable.
• Automated Review Tools: Leverage automated review tools that can scan AI-generated tests for common errors or anomalies. These tools can flag potential issues for human review, reducing the workload on testers.
• Collaborative Review Platforms: Implement collaborative platforms where multiple testers can work together to review and validate AI-generated tests. This distributed approach can make the task more manageable and ensure thorough coverage.
• Interactive Dashboards: Use interactive dashboards that provide insights and summaries of the AI-generated tests. These dashboards can highlight areas that require attention and allow testers to quickly navigate through the tests.

By employing these tools and strategies, your team can ensure that AI-driven test generation remains accurate and relevant, while also making the review process manageable for human testers. This approach helps maintain high standards of quality and efficiency in the testing process.

Ensuring Quality in AI-Driven Tests

Some best practices for high-quality AI testing include:

• Use Advanced Tools: Leverage tools like code coverage analysis and AI to identify and eliminate duplicate or unnecessary tests. This helps create a more efficient and effective testing process.
• Human-AI Collaboration: Foster an environment where human testers and AI tools work together, leveraging each other’s strengths.
• Robust Security Measures: Implement strict security protocols to protect sensitive data, especially when using AI tools.
• Bias Monitoring and Mitigation: Regularly check for and address any biases in AI outputs to ensure fair testing results.

The key to high-quality AI-driven testing is not just in the technology, but in how we integrate it with human expertise and ethical practices.

The technology behind AI-driven testing is designed to shorten the time from idea to reality. This rapid development cycle allows for quicker innovation and deployment of software solutions.

The future will see self-healing tests and self-healing code. Self-healing tests can automatically detect and correct issues in test scripts, ensuring continuous and uninterrupted testing. Similarly, self-healing code can identify and fix bugs in real-time, reducing downtime and improving software reliability.

Increasing Complexity of Software

As we manage to simplify the process of creating code, it paradoxically leads to the development of more complex software. This increasing complexity requires new paradigms and tools, as current ones will not be sufficient. For example, the algorithms used in new software, particularly AI algorithms, might not be fully understood even by their developers. This will necessitate innovative approaches to testing and fixing software.

This growing complexity will necessitate the development of new tools and methodologies to test and understand AI-driven applications. Ensuring these complex systems run as expected will be a significant focus of future testing innovations.

To address security and privacy concerns, future AI testing tools will increasingly run locally rather than relying on cloud-based solutions. This approach ensures that sensitive data and proprietary code remain secure and within the control of the organization, while still leveraging the powerful capabilities of AI.

You may also like…

Software testing’s chaotic conundrum: Navigating the Three-Body Problem of speed, quality, and cost

Report: How mobile testing strategies are embracing AI

The post The evolution and future of AI-driven testing: Ensuring quality and addressing bias appeared first on SD Times.

According to the company, the goal of this project is to make NLP simple. In creating the library, it wanted to avoid creating algorithms that navigate data structures based on the outputs of NLP models, and instead use Pandas DataFrames to represent NLP data.

The library includes Pandas extension types for representing natural language data and library integrations that convert the outputs of NLP libraries into DataFrames. 

Text Extensions for Pandas provides three key benefits: transparency, simplicity, and compatibility, according to IBM. 

“This project aligns with IBM’s goal to continually develop and deliver new natural language processing innovations, both in the open source community and through products like Watson Discovery and Watson Natural Language Understanding,” Frederick Reiss, chief architect at IBM Center for Open-Source Data and AI technologies, and Willie M. Tejada, chief developer advocate at IBM, wrote in a post. 

In addition, Text Extensions for Pandas integrates with IBM Watson Natural Language Understanding and IBM Watson Discover. 

The post SD Times Open-Source Project of the Week: Text Extensions for Pandas appeared first on SD Times.

Since making the GPT-3 beta available in June as an API to those who go through OpenAI’s vetting process, it has generated considerable buzz on social media. GPT-3 is the latest iteration of OpenAI’s neural-network-developed language model. The first to evaluate the beta, according to OpenAI, include Algolia, Quizlet and Reddit, and researchers at the Middlebury Institute.

Although GPT-3 is based on the same technology as its predecessor GPT-2, released last year, the new version is an exponentially larger data model. With nearly 175 billion trainable parameters, GPT-3 is 100 times larger than GPT-2. GPT-3 is 10 times larger in parameters than its closest rival, Microsoft’s Turing NLG, which has only 17 billion. 

RELATED CONTENT: Microsoft announces it will exclusively license OpenAI’s GPT-3 language model

Experts have described GPT-3 as the most capable language model created to date. Among them is David Chalmers, professor of Philosophy and Neural Science at New York University and co-director of NYU’s Center for Mind, Brain, and Consciousness. Chalmers underscored in a recent post that GPT-3 is trained on key data models such as Common Crawl, an open repository of searchable internet data, along with a huge library of books and all of Wikipedia. Besides its scale, GPT-3 is raising eyebrows at its ability to automatically generate text rivaling what a human can write. 

“GPT-3 is instantly one of the most interesting and important AI systems ever produced,” Chalmers wrote. “This is not just because of its impressive conversational and writing abilities. It was certainly disconcerting to have GPT-3 produce a plausible-looking interview with me. GPT-3 seems to be closer to passing the Turing test than any other system to date (although “closer” does not mean “close”).” 

Another early tester of GPT-3, Arram Sabeti, was also impressed. Sabeti, an investor who remains chairman of ZeroCater, was among the first to get his hands on the GPT-3 API in July. “I have to say I’m blown away. It’s far more coherent than any AI language system I’ve ever tried,” Sabeti noted  in a post, where he where he shared his findings.

“All you have to do is write a prompt and it’ll add text it thinks would plausibly follow,” he added. “I’ve gotten it to write songs, stories, press releases, guitar tabs, interviews, essays, technical manuals. It’s hilarious and frightening. I feel like I’ve seen the future and that full AGI [artificial general intelligence] might not be too far away.”

It is the “frightening” aspect that OpenAI is not taking lightly, which is why the company is taking a selective stance in vetting who can test the GPT-3 beta. In the wrong hands, GPT-3 could be the recipe for misuse. Among other things, one could use GPT-3 to create and spread propaganda on social media, now commonly called “fake news.” 

OpenAI’s Plan to Commercialize GPT-3
The potential for misuse is why OpenAI chose to release it as an API rather than open sourcing the technology, the company said in a FAQ.  “The API model allows us to more easily respond to misuse of the technology,” the company explained. “Since it is hard to predict the downstream use cases of our models, it feels inherently safer to release them via an API and broaden access over time, rather than release an open source model where access cannot be adjusted if it turns out to have harmful applications.”

OpenAI had other motives for going the API route as well. Notably, because the NLP models are so large, it takes significant expertise to develop and deploy, which makes it expensive to run. Consequently, the company is looking to make the API accessible to smaller organizations as well as larger ones.

Not surprisingly, by commercializing GPT-3, OpenAI can fund ongoing research in AI, as well as continued efforts to ensure it is used safely with resources to lobby for policy efforts as they arise. 

Ultimately, OpenAI will release a commercial version of GPT-3, although the company hasn’t announced when, or how much it will cost. The latter could be significant in determining how accessible it becomes. The company says part of the private beta aims to determine what type of licensing model it will offer. 

OpenAI, started as a non-profit research organization in late 2015 with help from deep-pocketed founders who include Elon Musk, last year emerged into a for-profit business with a $1 billion investment from Microsoft. As part of that investment, OpenAI runs in the Microsoft Azure cloud.

The two companies recently shared the fruits of their partnership one year later. At this year’s Microsoft Build conference, held as a virtual event in May, Microsoft CTO Kevin Scott said the company has created one of the world’s largest supercomputers running in Azure.

OpenAI Seeds Microsoft’s AI Supercomputer in Azure 
Speaking during a keynote session at the Build conference, Scott said Microsoft completed its supercomputer in Azure at the end of last year, taking just six months, according to the company. Scott said the effort will help bring these large models in reach of all software developers.

Scott likened it to the automotive industry, which has used the niche high-end racing use case to develop technologies such as hybrid powertrains, all-wheel drive and antilocking brakes. Some of the benefits of its supercomputing capabilities and the large ML models hosted in Azure enabled  by those capabilities  are significant to developers, Scott said.

“This new kind of computing power is going to drive amazing benefits for the developer community, empowering previously unbelievable AI software platforms that will accelerate your projects large and small,” he said. “Just like the ubiquity of sensors and smartphones, multi-touch location, high-quality cameras, accelerometers enabled an entirely new set of experiences, the output of this work is going to give developers a new platform to build new products and services.”

Scott said OpenAI is conducting the most ambitious work in AI today, indicating work like GPT-3 will give developers access to very large models that were out of their reach until now. Sam Altman, OpenAI’s CEO, joined Scott in his Build keynote to explain some of the implications.

Altman said OpenAI wants to build large-scale systems and see how far the company can push it. “As we do more and more advanced research and scale it up into bigger and bigger systems, we begin to make this whole new wave of tools and systems that can do things that were in the realm of science fiction only a few years ago,” Altman said. 

“People have been thinking for a long time about computers that can understand the world and sort of do something like thinking,” Altman added. “But now that we have those systems beginning to come to fruition, I think what we’re going to see from developers, the new products and services that can be imagined and created are going to be incredible. I think it’s like a fundamental new piece of computing infrastructure.” 

Beyond Natural Language
As the models become a platform, Altman said OpenAI is already looking beyond just natural language. “We’re interested in trying to understand all the data in the world, so language, images, audio, and more,” he said. “The fact that the same technology can solve this very broad array of problems and understand different things in different ways, that’s the promise of these more generalized systems that can do a broad variety of tasks for a long time. And as we work with the supercomputer to scale up these models, we keep finding new tasks that the models are capable of.”

Despite its promise, OpenAI and its vast network of ML models don’t close the gap on all that’s missing with AI. 

Boris Paskalev, co-founder and CEO of DeepCode, said GPT-3 provides models that are an order of magnitude larger than GPT-2. But he warned that developers should beware of drawing any conclusions that GPT-3 will help them automate code creation.

“Using NLP to generate software code does not work for the very simple reason that software code is semantically complex,” Paskalev told SD Times. “There is absolutely no actual use for it for code synthesis or for finding issues or fixing issues. Because it’s missing that logical step that is actually embedded, or the art of software development that the engineers use when they create code, like the intent. There’s no way you can do that.”

Moiz Saifee, a principal on the analytics team of Correlation Ventures, posted a similar assessment.  “While GPT-3 delivers great performance on a lot of NLP tasks — word prediction, common sense reasoning– it doesn’t do equally well on everything. For instance, it doesn’t do great on things like text synthesis, some reading comprehension tasks, etc. In addition to this, it also suffers from bias in the data, which may lead the model to generate stereotyped or prejudiced content. So, there is more work to be done.”

The post GPT-3: Advancing the understanding of cues for coding, writing appeared first on SD Times.

SuperGLUE was developed by AI researchers from Facebook AI, Google DeepMind, New York University and University of Washington. 

RELATED CONTENT:
AI ethics: Early but formative days
New machine learning inference benchmarks assess performance of AI-powered apps

“In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced one year ago, offered a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently come close to the level of non-expert humans, suggesting limited headroom for further research,” the researchers wrote on the SuperGLUE website. 

According to Facebook AI’s research, after its method for pretraining self-supervised NLP systems RoBERTa surpassed human baselines in simple multitask and transfer learning techniques, there was a need to continue to advance the state of the area. “Across the field, NLU systems have advanced at such a rapid pace that they’ve hit a ceiling on many existing benchmarks,” the researchers wrote in a post. 

SuperGLUE is comprised of new ways to test creative approaches on a range of difficult NLP tasks including sample-efficient, transfer, multitask and self-supervised learning. To challenge researchers, the team selected tasks that have varied formats with more “nuanced” questions that are easily solvable by people.

“By releasing new standards for measuring progress, introducing new methods for semi-supervised and self-supervised learning, and training over ever-larger scales of data, we hope to inspire the next generation of innovation. By challenging one another to go further, the NLP research community will continue to build stronger language processing systems,” the researchers wrote. 

The new benchmark also includes a new challenge, which requires machines to provide complex answers to open ended questions such as “How do jellyfish function without a brain?” The researchers explain this will require AI to synthesize information from various sources.

Another benchmark has to do with Choice of Plausible Alternatives (COPA), a causal reasoning task in which a system is given a premise sentence and must determine either the cause or effect of the premise from two possible choices. 

“These new tools will help us create stronger content understanding systems that can translate hundreds of languages and understand intricacies such as ambiguities, co-references and commonsense reasoning — with less reliance on the large amounts of labeled training data that’s required of most systems today,” Facebook wrote.

The post SuperGLUE benchmark challenges natural language processing tasks appeared first on SD Times.

In an effort to help developers build and deploy NLP systems, the company has decided to also open source the PyText framework as well as share its pretrained models and tutorials.

According to the company, with PyText it has been able to achieve faster experimentation, tackle text processing and vocabulary management at scale, and harness the PyTorch ecosystem for prebuilt models and tools.

“At Facebook, we’ve used this framework to take NLP models from idea to full implementation in just days, instead of weeks or months, and to deploy complex models that rely on multitask learning. At Facebook today, PyText is used for more than a billion daily predictions, demonstrating that it can operate at production scale and still meet stringent latency requirements,” the company wrote in a blog post.

The company explained that traditionally researchers and engineers have to tradeoff between frameworks built for experiments and frameworks built for production. “This is particularly true for NLP systems, which can require creating, training, and testing dozens of models, and which use an inherently dynamic structure. Research-oriented frameworks can provide an easy, eager-execution interface that speeds the process of writing advanced and dynamic models, but they also suffer from increased latency and memory use in production,” Facebook explained.

With the power of PyTorch 1.0, which addressed research and production obstacles with a single unified framework, PyText is able to bring PyTorch’s 1.0 features into natural language processing. Features include the ability to share models across different organizations within the AI community, prebuilt models of common NLP tasks such as text classification and language modeling, and contextual models to improve conversational understanding.

Facebook plans to use the framework in its own solutions to provide powerful features, flag policy-violating posts, perform translations, and improve products, it explained.

Going forward, the company plans to tackle end-to-end workflows for on-device models and provide multilingual modeling as well as other modeling capabilities that provide the ability to debug and improve distributed training.

“PyText has been a collaborative effort across Facebook AI, including researchers and engineers focused on NLP and conversational AI, and we look forward to working together to enhance its capabilities,” the company wrote.

The post Facebook’s open-source natural language processing framework appeared first on SD Times.

Finetuning refers to a transfer learning approach that is meant to take a model that is trained on one task and adapt it to be able to solve a different, but related, task.

“Most organizations have natural language processing problems, but few have the labeled data they need to solve them with machine learning,” said Madison May, Indico machine learning architect and cofounder. “Finetune lets them do more with less labeled training data. And it only requires a base level of IT experience.”

The project Finetune was developed to enable users to solve a variety of different tasks in text and document-based workflows. According to Indico, the project extends OpenAI’s original research and develop on improving language understanding with generative pre-training led by Alec Radford. OpenAI provided a model with general capabilities for document classification, comparison and multiple-choice question answering. The Finetune library packages up those capabilities for ease of use and adds document annotation, regression and multi-label classification.

“Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classification. Although large unlabeled text corpora are abundant, labeled data for learning these specific tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task. In contrast to previous approaches, we make use of task-aware input transformations during fine-tuning to achieve effective transfer while requiring minimal changes to the model architecture,” OpenAI researchers wrote in a paper.

The project also provides a quickstart guide, installation information, code examples, API reference and model configuration options.

“We have a vested interest in promoting the advantages of transfer learning and giving back to the open source community is a really productive way for us to do that,” said Slater Victoroff, co-founder and CTO of Indico. “I also want to acknowledge the important research and development work done by the team at OpenAI and Alec Radford. They are driving huge innovations in machine learning that really help accelerate the progress of companies like Indico.”

The post SD Times Open-Source Project of the Week: Finetune appeared first on SD Times.

According to Richard Socher, chief scientist at Salesforce who is leading the research team, while natural language processing is opening up new opportunities for machines, language understanding is becoming difficult because it connects and requires other areas of intelligence such as visual, emotional and logical areas.

“There are ambiguities and complexities in word choice, grammar, context, tone, sentiment, humor, cultural reference and more. It takes us humans years to master, so imagine the complexities of teaching a computer to understand these various facets in a single unified model. I’ve focused my career on this challenge,” he said.

The model is designed to tackle 10 different NLP tasks at once and eliminate the need to build and train individual models for each NLP problem.

“Deep learning has improved on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset and tasks. We introduce the Natural Language Decathlon (decaNLP),” the researchers wrote in the paper.

The 10 tasks are:

1. Question answering
2. Machine translation
3. Summarization
4. Natural language inference
5. Sentiment analysis
6. Semantic role labeling
7. Relation extraction
8. Goal-oriented dialogue
9. Semantic parsing
10. Common sense pronoun resolution

Socher is calling the model “the Swiss Army Knife of NLP” because of its ability to compact numerous tasks into one tool. According to Socher, traditional approaches require a customized architecture for each task, hindering the emergence of general NLP models.

“If you look at the broader landscape of AI, there’s been progress in multitask models as we’ve slowly evolved from focusing on feature engineering to feature learning and then to neural-architecture engineering for specific tasks. This has allowed for a fair amount of NLP improvements, but what we’re really looking for is a system that can solve all potential tasks,” said Socher.

This brought the team to an additional line of thought of having a dataset that is large enough to cover all tasks. For example, in computer vision, ImageNet is a large dataset that includes many visual categories, but there was not an equivalent dataset for NLP, so they set out to change that, Socher explained.

“DecaNLP has the potential to change the way the NLP community is focusing on single tasks. Just like ImageNet spurred a lot of new research, I hope this will allow us to think about new types of architectures that generalize across all kinds of tasks,” he said.

In addition, Socher explains that DecaNLP’s multitask question answering model can tackle unknown, never seen before tasks, which can lead to better chatbots and a broader range of new tasks.

Going forward, the team will continue to work on this model. According to Socher, all NLP tasks can be mapped to question answering, language modeling, and dialogue systems, so it is important to improve the performance of those within decaNLP.

“I hope that providing a powerful single default NLP model will also empower programmers without deep NLP expertise to quickly make progress on new NLP tasks, languages and challenges. This in turn will mean that products and tools we can speak to and interact with will become more broadly available,” he said.

The post Salesforce Research aims to capture the nuances of natural language processing appeared first on SD Times.

Features include: Espresso improvements, ProviderTestRule, Grant Permission Rule, Android Test Orchestrator, and AndroidJUnitRunner.

In addition, the company also revealed an update to its Playbook app for developers. The app provides developers with knowledge about features, best practices and strategies for succeed in Google Play. Updates included: a simplified user experience, better content discovery, and automated notifications.

Wit.ai to get rid of Bot Engine
In an effort to focus solely on purse natural language processing, Wit.ai announced it is sunsetting Bot Engine, deprecating Stories UI and releasing a new natural language processing solution. The company is launching the Built-in NLP for Messenger as part of the Messenger Platform 2.1. The new solution is designed to find a user’s meaning and information in messages and translate it to its bot.

Going forward, the company plans on improving its NLP technologies, make it easier to collaborate with the community, and help other platforms leverage the NLP API.

Node-ChakraCore updated
Microsoft announced a new preview release of Node-ChakraCore, based on Node.js 8 and available on Windows, macOS and Linux. Node-ChakraCore is designed to extend the reach of Node.js to Windows 10 IoT Core, according to the company.

“From the beginning, it’s been clear that in addition to growing the reach of Node.js ecosystem, there’s a need to address real problems facing developers and the Node.js ecosystem through innovation, openness and community collaboration,” Arunesh Chandra, senior program manager for Chakra, wrote in a post.

Updates include: Full cross-platform support, support for Node.js API, Node,js on iOS, and time-travel debugging.

MapR and Talend work towards GDPR data lake solution
MapR and Talend announced they are working together on a new solution to help customers address challenges and requirements for the European Union’s (EU) General Data Protection Regulation (GDPR) legislation. The offering will give companies the ability to create a governed data lake which meets data storage, inventory, and security requirements of the GDPR.

“The path toward GDPR compliance does not have to be complicated, but organizations do need to act now,” said Ciaran Dynes, senior vice president of products for Talend.  “Working with MapR, we are helping customers deliver transparency through proper metadata management practices; establish a collaborative approach to data governance; and modernize their data platform to support data lake development  that will ensure full compliance with GDPR.”

The combined solution will address compliance challenges related to: Data classification, data capture and integration, data anonymization, self-service data curation and certification, and more.

Samsung surpasses Intel in semiconductors
According to an AP report, Samsung Electronics has passed Intel as the leading maker of computer chips. Intel held a two decade reign as “king” of microchips and semiconductors, according to the report.

The report also states that Samsung said its semiconductor business recorded $7.2 billion in operating income on revenue of $15.8 billion in the quarter. Intel earned $2.8 billion on sales of $14.8 billion, and analysts expect the U.S. chipmaker to report $14.4 billion in quarterly revenue.

Patrick Moorhead, principal analyst with Moor Insights & Strategy, said in the AP report that he is not surprised Samsung surpassed Intel, and Intel may be able to catch up when its memory output is at full production capacity in about six months.

The post Android Testing Support Library 1.0, Node-ChakraCore, and MapR and Talend’s GDPR data lake solution — SD Times news digest: July 28, 2017 appeared first on SD Times.