Tara AI

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Tara AI is an AI-powered platform designed to enhance software engineering processes by providing actionable insights and metrics for better decision-making.

How Tara AI can help you:

Unify and enrich engineering activity without altering your teams’ workflows.
Provide real-time insights and alerts on project delivery and efficiency.
Aggregate performance data to uncover meaningful insights at both team and project levels.
Offer improved visibility into hurdles in engineering operations with real-time notifications.
Enable prioritization of initiatives that deliver actual customer value by assessing impact at the feature and project level.

Why choose Tara AI: Key features

Integration with issue tracking and Git source control for comprehensive insights.
Real-time visibility across the development cycle with active syncing to current tools.
Detailed analytics on engineering efficiency and customer delivery.
Tools for effectively communicating project status and identifying areas for improvement.

Who should choose Tara AI:

Software engineering teams looking to measure and optimize their workflows.
Engineering leaders needing a comprehensive overview of delivery metrics.
Organizations aiming to align engineering efforts with customer value.

About Tara AI

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https://tara.ai

Release Date

March 2024

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GitHub survey: AI coding tools deliver multiple benefits to developers in India, but company adoption lags

81% of respondents in India reported a perceived increase in code quality when using AI coding tools, and majority believe these tools enhance ability to meet customer needs GitHub, the world's leading AI-powered developer platform, today released new research highlighting how software professionals are putting AI coding tools to use, and the impact it's having on their teams, code, and organisations. The survey reveals that 81% of developers in India have experienced a perceived increase in code quality due to AI coding tools, and 66% believe these tools will enhance their ability to meet customer requirements, among other benefits. However, only 40% say their companies are actively encouraging the use of AI coding tools, highlighting an opportunity for organisations to better support their developers' interests. "The potential of AI-driven software development is undeniable," said Kyle Daigle, Chief Operating Officer at GitHub. "However, individual AI usage isn't enough. Organisations need to operationalise AI throughout the software development lifecycle to boost collaboration, creativity, and modernisation. By prioritising a strategic approach that balances innovation, security, and organisational alignment, we can unlock its full potential. This is an exciting time for engineering leaders-in India and globally-to embrace these advancements and propel their teams forward," Daigle added. The survey of 2,000 software professionals-which includes 500 respondents each across India, the U.S., Brazil, and Germany at organisations with 1,000 or more employees-identified several key benefits that respondents associate with using AI coding tools in software development. Key insights from respondents in India include: Improved code quality. 81% of respondents in India reported a perceived increase in code quality when using AI coding tools. This aligns with research GitHub conducted with Accenture over the past year to analyse the impact of GitHub Copilot on enterprise engineering teams, and its impact on perceived code quality.Respondents anticipate AI will enhance code security and development efficiency. There is universal anticipation (100%) among survey respondents in India that AI coding tools will improve code security. Notably, the highest expectation of a "significant improvement" across all respondents globally was in India, with 41% expressing this view.Improve ability to meet customer requirements. The majority of respondents in India (66%) expressed optimism about the potential of AI coding tools to moderately improve or significantly enhance their ability to meet customer requirements. This trend was consistent across various industries, suggesting a widespread expectation of benefits from generative AI.Easier to work with new programming languages, and understand existing codebases. A large portion (69%) of respondents in India reported that these tools make it "easy" to adopt a new programming language or understand an existing codebase. Notably, 28% of those in India highlighted that AI coding tools made it "very easy" for them to adopt a new programming language or understand an existing codebase.Test case generation. 99% of respondents in India stated their organisations have experimented with using AI coding tools to generate test cases. The majority (75%) of respondents reported their organisations use AI tools for test generation at least "sometimes."Proficiency in AI coding tools is seen as a major asset by job seekers. Nearly all respondents in India (99%) believe this skill makes them more attractive candidates, underlining the growing importance of AI across various fields. Notably, 56% in India believe this expertise significantly boosts their employability. Despite the reported benefits of AI coding tools by a high portion of respondents in India, a smaller percentage said their companies are actively encouraging adoption or allowing the use of AI tools, highlighting room for progress. In fact: 99% of respondents surveyed in India reported using AI coding at work at some point.In contrast, a much smaller percentage (40%) of respondents in India said their companies actively encourage and promote AI tool adoption. An additional 39% of respondents in India report that their organisations allow the use of these tools but offer limited encouragement. To maximise the benefits of these tools, organisations should have a roadmap, a clear strategy, and policies in place to ensure wider adoption happens through building trust and driving measurable performance metrics. GitHub's latest research highlights clear benefits of AI-powered software development. By prioritising a strategic approach that balances innovation, security, and organisational alignment, organisations can unlock its full potential. Learn more about the research in GitHub's blog here. Methodology This report draws on a survey conducted online by Wakefield Research on behalf of GitHub from February 26, 2024 through March 18, 2024 among 2,000 non-student, enterprise respondents in the U.S., Brazil, India, and Germany who are not managers and work at companies with 1,000-plus employees. This broke down to 500 respondents in each market and included people who identified their job titles as software engineers, developers, programmers, data scientists, and software designers. About GitHub As the global home for all developers, GitHub is the world's leading AI-powered developer platform to build, scale, and deliver secure software. Over 100 million people, including developers from 90 of the Fortune 100 companies, use GitHub to build amazing things together across 420+ million repositories. With all the collaborative features of GitHub, it's never been easier for individuals and teams to write faster, better code.

CXOToday.com

Wed, 21 Aug, 6:06 PM UTC

Crowdbotics introduces new AI-powered tools to address key software development challenges - SiliconANGLE

Crowdbotics introduces new AI-powered tools to address key software development challenges Code reuse platform startup Crowdbotics Inc. today unveiled new capabilities for its artificial intelligence-powered application development platform that are aimed at addressing challenges in creating applications. The new capabilities have been designed to address issues such as the fact that 70% of development project failures are the result of poor or missing requirements, coupled with inconsistent architecture decisions slowing down application delivery. Thrown into the mix is the issue of accurately predicting cloud costs for new applications, which results in uncertainty and risk. The company's existing platform includes features such as turning simple text prompts into a comprehensive, high-quality product requirement document or PRD that bridges the gap between the business stakeholder's needs and corresponding engineering specifications. Starting with well-constructed PRDs, Crowdbotics argues, teams significantly accelerate the development process while mitigating and even eliminating most of the risks. The new capabilities announced today start with Enterprise Context, which allows information technology leaders and architects to define and enforce corporate standards, such as development languages, frameworks, application programming interfaces and compliance requirements, during the creation of a PRD. The feature enhances consistency, predictability and speed in PRD creation, making it invaluable for large enterprises and system integrators who are managing extensive application portfolios. The next new capability, Technical Recommendations, uses AI to break down project requirements into functional specifications that deliver architectural and design recommendations for front-end, back-end and data architectures. It also suggests services and third-party APIs based on the organization's Enterprise Context, such as recommending Microsoft Entra ID for authentication when Azure is preferred. The outcome accelerates project planning while reducing risk by enforcing standards and consistency. The third new capability, Application Code Generation, leverages data from the PRD, Enterprise Context and Technical Recommendations to generate up to 50% of the required code and, in doing so, eases the workload for developers. The capability automates tasks such as CRUD -- create, read, update, delete -- operations and API handling while also implementing essential business logic. The feature maximizes accuracy and code coverage, marking a significant step toward AI-generated feature-complete applications. The next new feature, Cloud Consumption Cost Estimation, uses generative AI to predict monthly cloud costs for applications to help companies address the complexity of cloud hosting pricing models. Through the provision of cost data early in the development process, the capability allows engineers to make informed architectural decisions and avoid expensive, hard-to-correct mistakes later in the project. The last two new capabilities include Jira Integration and Azure Deployer. With Jira Integration, Crowdbotics ensures seamless compatibility with customers' existing DevOps tools, allowing PRDs to be easily turned into project plans managed through Jira, Azure DevOps or other platforms. The integration maximizes the platform's productivity benefits without disrupting the current DevOps infrastructure or workflows. Azure Deployer streamlines the deployment process by enabling organizations to deploy applications to Azure through the push of a button. Developers can use the feature to go from high-level requirements to code generation and deployment without writing infrastructure as code or managing continuous integration/continuous delivery pipelines. Crowdbotics handles the infrastructure setup to ensure smooth updates and a simplified deployment process. "We should never accept that 70% of all applications will fail," said founder and Chief Executive Anand Kulkarni. "The Crowdbotics Platform ensures development teams have access to more information than ever before to capture business and user requirements, make intelligent technology decisions, and even automate the writing of high-quality code, so companies can achieve their goals and drive success."

SiliconANGLE

Thu, 26 Sept, 2:02 PM UTC

AI Code Review Tools: Should You Use Them? - DZone

Artificial intelligence (AI) is entering various industries, offering numerous advantages to fields like software coding and development. Today, many development teams have adopted AI code review tools and noticed how much faster they catch errors and improve code quality. Yet, with any new technology, there are questions about its limitations, especially with security and accuracy. AI coding tools are software solutions that leverage artificial intelligence to analyze and improve code. Traditionally, code reviews require peers or senior developers to conduct this process manually, but it can be time-consuming and prone to human oversight. AI code review tools automate much of this process by using machine learning and natural language processing to detect anomalies, bugs, and security vulnerabilities in the code. These tools work by scanning code quickly to find common errors, security gaps, and inefficient logic. Then, it will provide suggestions for improvements. Tools like GitHub Copilot, CodeRabbit, and Codium AI can analyze lines of code and flag problematic areas. They even offer fixes in real time or during the review process. The AI behind these tools requires data scientists to train them on massive datasets of code. This helps them learn from previous reviews and continuously improve suggestions over time. AI code review tools had a market size of $4.86 billion in 2023, and researchers project it to grow at a compound annual growth rate of 27.1% from 2024 to 2030. This expansion suggests that more industries will use AI to streamline development and workflows and enhance code quality. When developers use AI code review tools, many find they offer several of the following advantages: Humans reviewing code vary in attention to detail and approach based on experience. Plus, the task itself takes time, which can cause burnout and make it more difficult to find flaws. Yet, with AI tools, developers can streamline the process as it maintains a uniform standard of analysis across all reviews. They apply the same criteria to every piece of code, ensuring that vulnerabilities or errors do not exist. As such, they can reduce overlooked instances due to human error or fatigue. This consistency helps development teams maintain a higher standard of code quality, especially in large projects that require several team members. The rate at which these tools operate is another major benefit. Since manual code reviews are time-consuming, AI solutions can analyze large amounts of code in a short period. With their instantaneous speed, they can identify issues in a fraction of the time it would take a human. This quick analysis allows developers to receive real-time feedback, enabling them to optimize their code as they write it. Developers are always on a time crunch, from dealing with unrealistic deadlines to fixing bugs and meeting new requirements. With limited availability to review code, developers can turn to AI tools whenever needed. As a result of their 24/7 availability, they can complete their reviews right on time. This means professionals can maintain their momentum on projects and address issues as soon as they arise. AI code review tools may have many advantages, but it is important for those working with code to consider their downfalls. A pressing concern with AI code reviewers is they come with security risks. While these tools can identify vulnerabilities and improve code quality, they are not foolproof. In fact, Snyk's experience with AI code review tools revealed that these systems suggested insecure code. However, its survey found that 75.8% of respondents believed AI-generated code was more secure than human-written code. Many developers feel confident about using these tools, but this may come from an increased need to understand how AI operates and its security concerns. While AI can be an asset, the same survey also revealed that 56% of developers admitted that these software solutions sometimes or often introduce coding issues like security vulnerabilities. Therefore, it is important to educate technology teams about the limitations of secure development. Meanwhile, proper oversight is key, as it still needs human reviewers to guarantee reduced vulnerabilities. AI tools have yet to reach perfection, as they can sometimes flag non-issues as potential problems. Or, they may fail to identify genuine code vulnerabilities. False positives can frustrate developers, as they force them to spend unnecessary time addressing warnings that are not a real threat to the code. Over time, this can lead to "alert fatigue," causing software professionals to dismiss valid warnings. Conversely, false negatives are even more concerning because they allow actual code flaws to slip through the cracks. This issue may lead to inefficient or insecure code once it is live. AI often lacks the ability to understand the broader context or intent behind the code. This can lead to inappropriate suggestions or missed issues that a human reviewer would likely catch. For example, AI tools may flag certain parts of the code as inefficient or redundant without understanding why a developer made those choices to meet a certain requirement. Additionally, when reviewing specialized code, the tool may overlook dependencies that are critical to the application's proper functioning. This lack of contextual awareness can lead to issues for developers when they must ignore irrelevant feedback. The benefits are clear with AI review tools: However, it is crucial to use them with their limitations in mind. Given their drawbacks, AI tools are best for pre-reviews rather than full code reviews. In other words, they can be highly effective in catching surface-level issues early on, but a project should still require thorough human review. When using AI for code review, developers can take advantage of these tools' full benefits. However, they do have limitations. That is why creators should use them thoughtfully. Leveraging their strengths while staying mindful of their weaknesses will allow for a more efficient and secure development process.

DZone

Thu, 17 Oct, 8:02 PM UTC

Ada v3 a Personal AI Assistant Built for Engineers

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The engineering landscape is rapidly evolving with the integration of AI agents and reasoning models, transforming how tasks are managed and decisions are made. These AI tools not only automate routine processes but also enhance the precision and consistency of your work, freeing up valuable time for innovation and strategic problem-solving. Ada v3 represents a significant step forward in AI-assisted engineering. This sophisticated personal AI assistant, still under development, is carefully designed to address the complex needs of today's engineers. With a suite of powerful features, Ada v3 is set to transform how you approach engineering tasks. Key capabilities of Ada v3 include: By integrating Ada v3 into your daily workflow, you can expect substantial improvements in both productivity and efficiency. This AI assistant excels at handling routine tasks, allowing you to focus your expertise on solving complex engineering challenges. 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Tools like Ada v3, generative AI, and sophisticated AI agents are set to redefine engineering practices across various disciplines. Key trends shaping the future of AI in engineering include: The orchestration layer will play a crucial role in managing these diverse AI tools across different engineering domains, making sure they work in concert to achieve overarching project goals. As these technologies continue to evolve, engineers who embrace and master AI-assisted workflows will be well-positioned to lead innovation in their fields. AI technologies offer the potential for significant productivity improvements in engineering, with gains of 2x, 5x, and beyond becoming increasingly achievable. By strategically aligning AI tools with critical engineering tasks, you can maximize efficiency and output in ways previously unattainable. 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Tata Communications Unveils Kaleyra AI: Revolutionizing Customer Interactions with AI-Powered Solutions

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