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.

Additionally, Google is revising its spam policies to more effectively exclude low-quality content from its search results. The updated policies target specific types of undesirable content, including websites that have expired and been repurposed for spam, as well as the proliferation of obituary spam. 

These measures are part of Google’s broader strategy to maintain the integrity of its search results and protect users from irrelevant or malicious content, thereby enhancing the overall user experience on the platform.

“This update involves refining some of our core ranking systems to help us better understand if webpages are unhelpful, have a poor user experience or feel like they were created for search engines instead of people. This could include sites created primarily to match very specific search queries,” Elizabeth Tucker, director of product management for Google, wrote in a blog post. “We believe these updates will reduce the amount of low-quality content on Search and send more traffic to helpful and high-quality sites. Based on our evaluations, we expect that the combination of this update and our previous efforts will collectively reduce low-quality, unoriginal content in search results by 40%.”

Google is enhancing its policy to tackle abusive content creation practices aimed at manipulating search rankings through scaled content production, regardless of whether it is generated by automation, humans, or a combination of both. 

This update aims to target and mitigate the impact of low-value content created en masse, such as webpages that appear to provide answers to common searches but ultimately fail to offer useful information. This initiative reflects Google’s commitment to improving the quality of content surfaced by its search engine, ensuring users receive relevant and valuable information, according to Google.

The post Google updates Search algorithm to help reduce spam and low-quality content appeared first on SD Times.

This approach enables organizations to adapt quickly to changes, making application and workflow automation deployment faster and easier. Additionally, Creatio Quantum introduces new components, generative AI, and a governance app, providing users with greater freedom in automation, the company explained.

With the introduction of Quantum, Creatio now fully embraces a composable approach, constructing all product functionality using pre-built components and blocks. At its most fundamental level, this consists of elements like widgets or sets of fields.

Creatio has disassembled all features of its CRM suite into components, blocks, and apps. This not only allows users to construct distinctive solutions using pre-made components and blocks but also enables them to utilize pre-built apps to meet their specific needs.

A highly sought-after feature, generative AI, has now been integrated into all of Creatio’s products, using models from OpenAI. According to Creatio, generative AI complements and expedites the no-code development process by automatically generating templates, components, or entire applications based on user-provided text input. This significantly reduces the time and effort required by no-code app creators to transform basic requirements into prototypes.

The launch of Quantum takes a further step in enabling businesses to adapt, create, and innovate with unparalleled efficiency, Creatio added.

The post Creatio shifts to composable architecture for its no-code platform appeared first on SD Times.

Slack AI is built natively into Slack on its trusted foundation, grounded in a company’s collective knowledge found in Slack, and easy to access in the flow of work.

Using AI, channel recaps in Slack provide quick summaries of important information from any channel. These recaps not only help users focus on the most crucial details but also save time by allowing them to create status reports and extract key themes from different channels.

Thread summaries in Slack allow users to efficiently catch up on lengthy discussions within a thread with just one click. This feature is particularly useful when teams are actively involved in incident resolution, decision-making, or idea brainstorming, as it streamlines the process of staying informed.

Also, “Search answers” in Slack assists customers in leveraging their conversational data and the context it provides from the collective expertise and experiences within the organization. Users can ask questions, and the search feature not only provides results containing relevant messages, files, and channels but also includes an AI-generated summary to enhance understanding.

Slack has also added automation capabilities to empower anyone to automate without code using a new and improved Workflow Builder that offers connectors from companies like Google Workspace, Atlassian, and Asana. Users can also build and deploy custom apps, hosted in Slack, and Slack will take care of the hosting, eliminating infrastructure overhead and ensuring data is stored securely in Slack.

With Slack lists, customers can manage, track, and triage work while in the flow of communication by tracking projects, managing launches, and reviewing approvals and requests. 

“At Slack, we’re taking a collaboration-first approach to delivering an intelligent productivity platform in the age of AI and automation,” said Noah Desai Weiss, chief product officer of Slack. “We are focused on providing customers with a simpler, more delightful, and more efficient set of tools so every person can do the best work of their lives.”

The post Slack AI, Slack Lists, and new automation capabilities released appeared first on SD Times.

To understand what this looks like, it’s helpful to look at how we got here. Data management has come a long way over the past decade, evolving from Teradata and Informatica, to Hadoop, and now to the age of Snowflake, Databricks, and Google BigQuery. Today’s modern, cloud-based architecture has an alluring simplicity, but as the task of managing and maintaining it grows in complexity it undermines the very benefits it has set out to provide. We’ve made progress, but we’re not there yet.

An unwieldy mass of tools

Even with a modern cloud architecture, significant complexity remains. Data engineers have been given a vast array of new tools, but there are so many of these tools that they have become unwieldy. Data sources have multiplied, pipelines have become more intricate, and the resulting infrastructure is not self-aware, requiring significant manual maintenance. The challenges of the modern data stack outweigh the benefits, leaving teams disillusioned and overworked.

All roads lead to automation

When we look at the history of enterprise technologies, it becomes clear that all roads lead to consolidation, simplification, productization, and ultimately automation. Here are two examples that illustrate this:

• Early databases evolved into relational databases and standardized on the SQL language. Disparate functionality and features were consolidated into unified data management systems that now automate routine maintenance tasks, enhancing performance and reducing operational burden.
• More recently, provisioning and managing cloud infrastructure has been greatly simplified by automation tools like Terraform and Kubernetes, eliminating manual errors and complexity.

The modern data stack is ripe for a comparable wave of evolution, to reduce the complexity and free up engineers’ time so that they can work on projects that actually move the needle for the business. The post-modern data stack requires a more self-aware, unified architecture that opens the door to intelligent automation.

Characteristics of the post-modern data stack

The post-modern data stack is powerful because it takes a holistic and synergistic approach, with characteristics including:

• Optimized Metadata Collection and Storage: Centralizing metadata allows engineering teams to streamline the entire ingest-to-observability process, allowing systems to be automated and optimized based on a shared metadata backbone.
• Intelligent Pipelines: Intelligent pipelines can adapt to changes in code and data, reducing dependencies and allowing for more efficient data processing with minimal human intervention.
• Value-Driven Data Products: By reducing manual work required by the modern data stack, teams can devote their resources and expertise to building new data products that drive value and meaningful outcomes for the business.

Unleash the power of your data – and your teams

The modern data stack may not have been the panacea we were hoping for but the path forward is clear. By prioritizing fewer, more unified tools and embracing automation powered by centralized metadata, engineering leaders can unlock not just the full potential of their data but the full potential of their data teams. 

The history of technology shows that all roads lead to automation. The post-modern data stack is the vehicle for getting there, offering a path to increased productivity and greater innovation. The future of data management is here, and it’s intelligent, streamlined, and designed for value.

The post The post-modern data stack: Why all roads lead to automation appeared first on SD Times.

It’s time to shed light on the human side of automation – an aspect that goes beyond mere work efficiency gains. Yes, automation streamlines processes, accelerates software development, reduces human error, and boosts consistency in quality, but the underlying goal should not be to increase the workload or create an environment of perpetual busyness. Instead, it should serve as a catalyst for creating a harmonious balance between work and personal life. An underreported ROI of automation lies in its ability to not only free us from the burden of monotonous and time-consuming tasks but to give us back time to have a life.

The testing world has been transformed by automation. Developers can redirect the time they once spent on manual testing to more meaningful pursuits. But what about other teams? For me, the positive effects of automation have rippled throughout my organization. With a more efficient workflow, projects are being delivered faster and with higher quality. The bread and butter of my job is writing high-quality content to support sales, so AI tools like ChatGPT and Google Bard have helped me quickly come up with blog ideas, webinar titles, summarize and make sense of large amounts of data, and more.

If you’re an API developer, how time consuming is it for you to have to test your APIs manually? Is there any benefit to you performing the task that way? How many hours would it save you, your team, and your business to be able to automate that process?” 

If you’re a performance tester, what is your most time-consuming task that you do manually? Is it possible to automate even a portion of it?

Certainly, AI engineers can automate more of their tasks. For example, debugging software, predicting future issues, and filtering low-tier incidents no longer have to be manual tasks. 

The desire for work-life balance is universal among employees. Mental well-being platform Yerbo’s recent study of more than 36,000 IT professionals in 33 counties found that two in five workers are at high risk of burnout because of long hours, demanding workloads, and conflicts in work-life balance. 

As managers, it is vital to recognize the significance of this aspect and foster an environment that values the well-being of team members. By actively understanding the automation tools that teams require to effectively manage their workloads, maintain work-life balance, and embrace personal interests, managers can play a pivotal role in promoting happier, healthier, and more productive teams.

Organizations should give all their teams access to whatever automation tools they need that will enable them to do their jobs more efficiently and whatever enables others to help them do theirs – so no one burns out, quits, or feels like their job situation is forever doomed.

When developers or any team member experiences reduced stress and gains more time for personal pursuits, team morale improves. A sense of fulfillment in personal life often translates into increased creativity and enthusiasm within the workspace, leading to higher job satisfaction and better project outcomes.

Moreover, prioritizing work-life balance through automation initiatives nurtures longer-tenured and engaged teams. By empowering others with automation tools, organizations can mitigate the risk of employee turnover and create an environment where employees feel valued, supported, and equipped to thrive in their roles for the long term.

However, achieving the full potential of automation requires strategic planning and implementation. The focus should not solely be on the technical aspects of automation but also on the well-being of the those who will be utilizing these tools. Providing the right training, support, and access to automation tools tailored to the team’s needs are critical steps in ensuring the success of automation initiatives.

Automation is not just a means to improve efficiency; it is a transformative force that can lead to a more balanced and fulfilling life for all teams. The journey toward a more harmonious work-life integration begins with understanding the needs of each team, providing them with the automation tools that enable a healthier and more satisfying professional journey.

Organizations and managers hold the key to shaping a culture that values the well-being of their most valuable asset – their employees. Through a thoughtful approach to automation and a genuine commitment to work-life balance, all teams can thrive in an environment where personal growth and professional success go hand-in-hand.

Whether it’s dedicating more hours to hobbies, engaging in quality time with family and friends, or focusing on self-care, automation presents an opportunity for us all to craft a more balanced and fulfilling life. 

The post The human side of automation: Reclaiming work-life balance appeared first on SD Times.

The answer lies in a familiar tactic: automation. Development teams are increasingly adept at automating huge chunks of their work, from setting up the necessary infrastructure environments to building, integrating, testing, and releasing software. Call it DevOps or CI/CD, the tactic is the same: ruthlessly automate mundane or repetitive tasks. To ensure compliance requirements don’t hinder development, IT leaders must similarly prioritize automating data profiling and protection as a normal part of their development pipelines. 

The growing impact of data privacy on software development

The regulatory landscape for data privacy and protection continues to grow, resulting in ever-increasing, and increasingly complex, compliance requirements. In fact, McKinsey found three quarters of all countries have adopted data localization rules, which have “major implications for the IT footprints, data governance, and data architectures of companies, as well as their interactions with local regulators.” 

Existing data privacy regulations such GDPR in the EU and HIPAA in the US, updates to older mandates (e.g., the recently updated Federal Trade Commission (FTC) Safeguards Rule mandated by the Gramm-Leach-Bliley Act), and new and emerging laws (e.g., Virginia Consumer Data Protection Act, Canada’s Consumer Privacy Protection Act) all threaten to slow down software development and innovation by adding layers of security requirements onto the development process. 

Even without the introduction of new privacy mandates, the impact of data privacy and security requirements on development is almost certain to grow. For one thing, development and testing environments have proven to be rich attack targets for threat actors. From source code management systems to infrastructure such as virtual test servers to the test data itself, all are attractive targets for bad actors seeking to compromise systems and data. Add in the many different cloud development platforms like Salesforce and SAP, and it’s clear there is plenty of opportunity for a hungry hacker with nefarious intentions. 

Therefore teams must ensure the entire application lifecycle is secure, including development and test environments, whether on-prem or in the cloud. How do IT and security accomplish this without slowing development and release cycles? The answer lies in test data automation.

Test data management meets DevOps

The software development process is reliant on access to fresh test data. Traditional methods for managing and provisioning test data are typically manual and tremendously slow – think ticketing systems and siloed, request-fulfill models that can take days or even weeks. These processes are very much at odds with modern development methods such as DevOps and CI/CD, which demand fast, iterative release cycles. 

This is where application innovation often grinds to a halt. DevOps and DevSecOps processes have automated quality assurance testing and security and compliance testing throughout the CI/CD pipeline. But data provisioning and governance has remained a manual and time-consuming practice. Enter DevOps test data management (TDM) which automates the “last mile” of DevOps and provides fast delivery of lightweight, protected data in minutes instead of days, weeks or months. With DevOps TDM, organizations can accelerate development and testing, and in turn, can increase compliance and innovation.

Just how much can DevOps TDM accelerate software innovation? Consider one example from Dell Technologies. The technology giant’s developers needed quick access to fresh test data, but, like many other organizations, manually provisioning the data was a slow, tedious process. 

By automating DevOps test data management, Dell significantly increased the speed and efficiency of its test data provisioning and governance. Now, 92% of Dell’s ~160 global, non-production database environments are refreshed automatically on a bi-weekly basis. Developers can now initiate releases through their CI/CD pipelines in just 17 minutes. This has allowed the Dell team to run 6 million pipelines the first quarter of 2022, and more than 50 million since they implemented this standardized, automated approach. 

Shrink the surface area of private data

Antiquated approaches to test data management often rely on scripts or otherwise poorly integrated processes that result in the proliferation of sensitive data throughout the enterprise. It’s not uncommon for each development environment to have its own copy of sensitive production data for testing purposes. And often developers maintain their own copies for coding and unit testing. Many enterprises end up with hundreds or even thousands of uncontrolled copies of sensitive data.

Privacy mandates and security policies treat these copies of sensitive data no differently than the production databases from which they were spawned. Sensitive data such as personally identifiable information (PII) or cardholder data must be secured to the same degree, whether or not it’s in production. This often translates into requiring encryption both at rest and in transit, as well as carefully managed access controls and other protections. And then there are the near-universal requirements for the right to be forgotten. Privacy mandates regularly require businesses to destroy personal data upon request. It does not matter where that data lives.

The solution is eliminating the replication of sensitive data through the use of data masking. To provide production-quality data to your teams and non-production environments without multiplying the burden of security and privacy protections, DevOps TDM approaches — when implemented properly — automate the masking of sensitive data. In effect, this step shrinks the surface area that you must protect. This reduces your compliance and security risks as well as the impact on your budget.

Quite simply, having less sensitive data strewn about your business means less you to protect. Automation can make that possible.

Starting small but thinking big

Automating with DevOps TDM may appear overwhelming at first. Where do you start? But this is one change where it is very easy to start small, automating test data delivery and masking for just one or a handful of applications. Many businesses begin by addressing their most sensitive environments and where CI/CD pipelines already exist, such as customer-facing apps. Here, the need for protection and the underlying automation framework (i.e., the DevOps toolchains) already exist.

But businesses should also think big as they evaluate solutions. The number of distinct data sources is likely to expand over time. You might have a SQL database on AWS today, but then add your Salesforce platform and mainframe DB2 into the mix in the future. Masking these data sources while preserving referential integrity across them may prove challenging but is essential to effective integration and user acceptance testing.

Ultimately, businesses centralize DevOps TDM while giving their development teams autonomy over the acquisition and use of test data. Centralization means you can apply policies for the masking of sensitive fields and use database virtualization to cost-effectively provision data. 

The benefits of DevOps TDM are substantial. Not only do businesses improve compliance and mitigate risks, they also speed up development and reduce costs. It represents one of those rare instances where a tradeoff between faster, better (safer) and cheaper is no longer required.

The post Don’t let data compliance block software innovation; automation is the key appeared first on SD Times.

“While organizations are building their businesses and deploying applications on the cloud, most teams are constrained by legacy processes which are creating slow, error-prone, and costly challenges due to the lack of a viable cloud-based testing solution,” said Suhail Ansari, the chief technology officer at Tricentis. “Tricentis Test Automation enables organizations to automate end-to-end quality for their integrated cloud-based solutions with faster speeds, no-code, and reduced test maintenance costs.”

Businesses can use it to test end-to-end business processes and complex business applications. They can also verify quality across their integrated platforms. 

The user-friendly SaaS-based solution allows users to quickly create automated tests without prior coding or test automation knowledge, ranging from functional UI to API/microservices testing and enables them to scale up accordingly.

With model-based UI test automation, users can build codeless, resilient, automated tests through a unique approach that separates the automation model from the underlying application.

Teams can text faster and at scale by running multiple tests in parallel across distributed infrastructures and VMs. Users can define test data and environmental coverage to prepare, configure, and orchestrate test cases for multi-app, end-to-end process testing.

The post Tricentis Test Automation marries low-code with testing appeared first on SD Times.

One of the most essential aspects of remaining competitive is adopting automation, and introducing artificial intelligence tooling into every area of the business. 

According to Dr. Vinod Vasudevan, CEO of Flytxt, a company that offers AI-driven solutions, bringing AI into the marketing side of the business is becoming increasingly important as these tools become more and more powerful. 

“Marketing tools are now offering capabilities with the promise that marketers can optimize marketing mix decisions in weeks rather than months,” Vasudevan said. “With AI, it is possible to… optimize a marketing campaign… the moment it is launched – with accurate customer and product intelligence derived based on historical performance data of similar campaigns or products in the market.”

Vasudevan expanded on this saying that one of the biggest benefits of AI tools for marketing is its ability to ensure ROI. He said that AI can be used to discover the most optimal mix of customer, product, and pricing in order to drive value and guarantee ROI.

Jessie Johnson, principal analyst of demand marketing at Forrester, said that in addition to revenue increases, AI marketing tools help organizations to create an essential feedback loop with their customers.

“And at the same time that we’re able to sense and respond to the needs of our audience, we’re also creating better marketers through the insights that they are capturing from their AI based tools,” she said.

According to Johnson, having these feedback loops in place with the help of AI is a key factor in achieving continuous development and improvement. 

On top of this, Vasudevan went on to explain that AI marketing tools can also work to ensure that compliance standards are met as well as offering heightened personalization.  

He said that personalization spans across several different areas. Whether it is simply personalizing the content, or going deeper and personalizing the semantics.

Johnson also touched on this. She said, “Personalization is a really key use case, along with thinking about the orchestration of the journey. So figuring out what tactic comes next and what the next best action is for both buyer and the seller.”

Johnson continued, saying that another of the most prominent use cases for AI in marketing tools is simplifying the process of properly targeting content.

“That is certainly one of the top use cases. How are we really reaching our audience with the right content, the right time, the right person, all of those things,” she explained. 

She went on to say that conversation automation is another essential element. Johnson explained that leveraging conversational AI allows businesses to communicate directly with both prospects and customers all across the customer lifecycle. 

“Things like intent monitoring, conversion optimization… and where we see that the most is on the website,” she said. “So, how are we dynamically generating that content experience on the website, based on what we know about that audience?”

She went on to say that there has also been a fair amount of buzz around generative AI and what it can do for marketing organizations. 

She explained the ability of artificial intelligence to manufacture images that can then be put into a content engine helps to deliver an even more customized experience for the user. 

With all of these benefits, though, Vasudevan explained that it is essential to keep user security top of mind.

“I also think it is very essential to build that trust and that confidence in the AI,” he said. “Fundamentally, all AI is built with a lot of data… and therefore, privacy becomes another important aspect.” 

He said that once organizations have a firm grasp on privacy, transparency, and explainability with AI tools, adoption will grow exponentially. 

Additionally, Johnson stressed that all of these use cases are only the tip of the iceberg. She believes that as more organizations begin to pilot this technology, the adoption of AI into marketing organizations will only grow as the capabilities continuously expand. 

Vasudevan also emphasized this, saying that marketers are now past the initial stage of recognizing the usefulness of AI and have moved into the phase of heightened adoption, to test out just how far this technology can go. 

The post How AI is being used to further marketing initiatives appeared first on SD Times.

The main goal of these use cases, and several others, is to support developers while they work to accelerate the delivery of new products. 

Automation is no longer just an option

Suzie Prince, head of product, DevOps, at the software development tool company Atlassian, explained that with today’s developers being tasked with so many different jobs, AI and automation work to simplify complexities as well as control tool sprawl. 

“Automation is really the only way for developers, specifically, to keep up,” Prince said. “But also we know that the best organizations use automation all over the place inside their software development life cycle…Unless a team has automated test speeds and automated deployments, there is no way that they can move to continuous delivery and therefore, they can’t really become these elite performers.”

Knowing this, adopting automation is no longer optional. Rather, it has become the next necessary, evolutionary step for every company. 

“All companies have to become AI companies in order to remain competitive,” said David DeSanto, Chief Product Officer at GitLab. “It’s not just about leveraging their own machine learning products, but leveraging products that use AI to be more effective and deliver software both faster and more securely.” 

Rajesh Raheja, chief engineering officer at the intelligent connectivity and automation company Boomi, built on this, saying that automated code scanning tools that can identify bottlenecks or errors in code are a way that many organizations are currently utilizing AI to speed up development.

“There are tools out there that can use algorithms to try and determine if what you have put into the code is correct in terms of legal implications with open-source software,” he explained. “But then it goes even deeper into product quality.” Raheja explained that manually performing these product quality tasks would take a tremendous amount of time. By bringing in this automation, the developer gets that time back and the process is accelerated.  

Anand Rao, global artificial intelligence lead at the business advisory firm PwC, went on to say that he is seeing AI and automation being applied to three different areas to accelerate development: data life cycles, software life cycles, and the model development life cycle.

“AI and automation really plays across all of that,” Rao said. “Not just to automate the software engineering process but also to automate the pipelines that connect the data to the AI.”

Where is automation most useful?

In Raheja’s experience, automation has had the highest impact on velocity when it is applied to quality.

“First, you have the automation in the pipelines that ensures that bad quality code does not get into production, so that already helps the team to not spend time fixing defects and retesting,” said Raheja. “Secondly, this can help companies fix tech debt… Quality automation allows developers to make much bigger and more inclusive changes than they otherwise would without the fear of breaking the system.” 

Furthermore, DeSanto explained that GitLab is currently utilizing automation in several other areas to provide for faster development speeds. These include identifying the right code reviewer, actual code creation, and intelligent code security. 

He expanded on this, saying that introducing AI into code security can help to speed up development because its main purpose is ensuring that the first time code is committed to a project, it is already secure. 

DeSanto noted that by building automation into security early in the process, code can be pushed to production quicker since it cuts down on the number of times developers will have to go back to earlier stages to fix a vulnerability. 

Additionally, when looking to add automation into security practices, Atlassian’s Prince emphasized that organizations will get the most out of this automation if it does not fall on the developer alone. 

She said the expectation that the average developer or operations team member will be able to double as a security expert is unrealistic and can end up having a negative impact on delivery speeds. 

“You have to really look for expert skills in your teams and also look for best-of-breed security tools… These are expert skills and this is a very specific area, so find the best tools and use their automation either in the coding or in your test automation suite,” Prince said. 

Empower developers, don’t replace them

Rao also mentioned that in order to get the most out of security automation there has to be an established way for a human to take control if the automation fails.

That being said, it is important to note that even with the usefulness of AI and automation, organizations should be wary of losing that essential human aspect of software development.

“I am a very strong proponent of ‘human in the loop’ systems,” Rao said. “So, essentially the AI is making the recommendations to the human, but the human makes the final choice and that choice is then implemented by the AI.”

According to Rao, this allows for automation to still play a major role in development without the potential for it to make decisions that the developer wouldn’t have made, thereby making the life cycle more efficient. 

Boomi’s Raheja also touched on this, explaining that AI and automation cannot think critically, so it cannot operate effectively without a human there to make those decisions.

“A business might have the goal of growing 50% year over year, or that they want their revenue to be a billion dollars, and how do you translate that into automation? The automation doesn’t really know what that means, so you definitely still need that particular human thinking,” he said. 

DeSanto went on to explain that when human developers work with automation, it can help to accelerate development because it works to get ahead of problems.

“There is a style or a nuance that could potentially be lost in generating code without context, and so we really see [security automation] as a way to empower developers,” he said. “This helps the developer from the beginning as opposed to giving them a writeup of problems after they’re done with the project.”

He added that while automation is essential to increase speeds, it was never meant to replace developers. 

Rather, DeSanto believes it has the potential to fight against developer burnout and result in an even more engaged team.  

“I see AI as a way to help make the existing staff more effective,” he said. “When you look at it from that point of view, you don’t lose that human aspect anymore, in fact it kind of becomes the most important thing.”

Additionally, according to Raheja, automation can be an incredibly useful tool to allow developers the freedom to work on what they actually want to work on. 

He said that the best way to do this is to automate the processes that are essential but that the developer finds monotonous. 

This enables developers to invest in the more interesting and aspects of the job, while the important but more tedious work is done through automation. 

Prince went on to explain that in her experience, security automation is the best example of human developers and AI needing to work in tandem.

“There are circumstances where a new vulnerability becomes available or a new way of exploiting software reveals itself, and that is where you would need that human expert,” she said. “They would then have to dive really deep into the software and do what we call black box testing or penetration testing, ” 

Automated testing

An important area in which AI can accelerate development is in the testing process. David DeSanto, VP of product at GitLab, explained that AI/ML tools can reduce the amount of noise developers see when working on unit tests.

Automation in testing, he said, can help developers go right to the unit or quality tests that are causing problems, while automatically getting rid of false positives. 

“Early in my career, cutting through all the noise and all the alerts developers get was essentially the challenge of the job for me,” DeSanto said. “If we can leverage AI to make that easier, you’re going to find better uptime, better engagement, and less stressed ops engineers.” 

According to DeSanto, several organizations have already brought automation into their testing practices in order to accelerate development and make the lives of team members easier. 

He cited GitLab’s most recent DevSecOps survey, noting that “37% of teams said that they use AI/ML today as a part of their software testing. That is up from 25%, with another 20% wanting to do it within the next year and the remainder wanting to roll it out in the next couple of years.”

With this, DeSanto explained that a company’s ability to automate and their ability to maintain a competitive edge are not just correlated, but causal. 

“App transformation, cloud migration, digital transformation, they’re all about how to get value from what my developer is building from when they start writing it through to when it is in the hands of the user,” he said. “A growing percentage of organizations are having to ship software continuously to really do that… that’s because they have to deliver the value faster and you can only do that if you automate.” 

The future of automation 

Even with the strides that have been made with AI and automation, Raheja believes there is still room for improvement in the future.

He said that the areas in which automation has the most potential for growth is standardization and maturity. He explained that for an organization to reap all of the benefits of AI, it needs to be both consistent and repeatable. 

Raheja said that some of this standardization has already been applied to automation in low-code tools, but generally, AI and automation still has a way to go before it reaches the optimal level of maturity. 

Prince also touched on this, saying that she believes development teams are still just scratching the surface of what this technology can offer.

She said, “We really have a long way to go with most organizations…Ultimately, I think that we are at the very beginning of businesses taking advantage of the power of automation and AI.”

The post Automation: The next evolutionary step toward elite performance appeared first on SD Times.