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3 Big Ways Artificial Intelligence Is Changing Software Development

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Agile technologies such as DevOps and continuous integration, continuous delivery (CI/CD) practices have brought about positive changes for software developers over the past decade. From faster time to market and greater collaboration between development and operations teams to fewer end-user issues and improved testing, DevOps has changed the way developers work. In a similar vein, artificial intelligence (AI) is poised to change how organizations and their engineering teams approach software development.

AI’s promising potential

Software engineers are tasked with creating solutions for the myriad problems, challenges, and everyday organizational tasks in most every industry imaginable. Ironically, they even develop the tools that make their development processes easier. AI is well-suited to helping software engineers develop these intelligent software tools because it can learn from and replicate human behaviors. Because of this, AI and machine learning algorithms can impact nearly all areas of software development.

Best uses for AI in software development

AI and machine learning have already made big impacts in software development. Here are three of the most important ways it is changing the development landscape and the evolving role of software engineers.

  1. Estimating delivery schedules — When development teams work together for long periods of time, they become fairly adept at estimating delivery times, although they may still encounter challenges due to a variety of influencing factors, including flawed code and changing user demands. AI can help development teams make more accurate estimates, even with the numerous and diverse factors that come into play. And as the AI programs gather more data and learn from other development projects, the accuracy of those estimates is likely to continue to improve.
  2. Project management — AI systems can take over daily project management tasks without the need for human input, according to The Next Web article. Over time, they can understand project performance and use that knowledge to form insights, complete complex tasks, and help human project managers make improved decisions.
  3. Testing and quality assurance — Developers are creating tools that use AI to detect flaws in code and automatically fix them, according to the Forbes article. This is the logical next step after testing automation and will likely lead to higher-quality software and improved time to market. Software engineers could have less involvement in testing mechanics but would shift their roles to approving and acting on test findings. In other words, AI could streamline software testing by providing the right data to the human engineers who can then make better decisions.

evolution of the programmer

Best practices and AI’s future

Based on these changes alone, it seems AI and machine-based learning are primed to disrupt the software development field. What does that mean for development company leaders, software engineers, and software development in general?

Overall, AI will likely help software development become better, faster, and less expensive. However, for this to happen, engineers would have to learn a different skill set so they could build AI into their development toolboxes. They’d need more data science skills and a better understanding of deep learning principles to reap the full benefits of machine-based learning. Also, instead of turning to logging and debugging tools to find and fix bugs, engineers would need tools that allow them to question the AI to find out how and why it reached a particular conclusion. In addition, AI could allow more tasks to run autonomously and require fewer daily management tasks. Finally, developers could use AI for routine tasks so humans can focus on what makes them human: thinking creatively according to the problem’s context, something that AI has not yet mastered.

Will AI eventually replace the human element in software engineering? Not likely, but it certainly has the potential to make development faster, more efficient, more effective, and less costly all while letting engineers and other development personnel focus on honing their skills to make better use of AI in their processes.

For the best in thought leadership on emerging technologies, look to the software development experts at Parkar Consulting & Labs. Contact the Parkar professionals today.

7 Metrics for Improving Software Engineering Efficiency and Productivity

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In today’s fast-paced technological landscape, tomorrow’s products are already at risk of being “old news.” To overcome lengthy production cycles, successful software developers are focusing on efficiency and productivity. Greater efficiency leads to greater productivity, accelerated product life cycles, shorter time to market and, ultimately, an increased bottom line. Engineering team managers can use many metrics to gauge efficiency and productivity along the way. But of course, the ultimate measure of a software application’s success is whether it meets end users’ needs and improves the organization’s business results.

Tracking the metrics

Keeping track of certain metrics is an important step in making sure your company’s software engineering team is performing as efficiently and productively as possible. Here are some of the most essential metrics for developers to track:

  1. Lead time — Lead time is a general measurement of the amount of time that passes from product concept to final delivery. It will depend on project complexity and the number of engineers working on the project, both of which will, in turn, affect project cost. By tracking project lead times, developers can better predict time to market for current and similar future projects.
  2. Impact — Impact measures how much changes to the code affect the overall project. It is also a measurement of how those changes impact the engineers who make them. This is an important metric to measure as code changes can lengthen the product’s time to market, making it costlier. In addition, developers could be dedicating too much time to making certain changes, negatively affecting their productivity and adding to such delays. Therefore, large changes, such as those that involve more code or files, and changes that are more difficult to make have higher impact scores.
  3. Churn — Churn is the percentage of time developers spend editing, adding to, or deleting their own code. A high code churn indicates rework and may mean something is wrong in the development process.
  4. Efficiency — Efficiency measures the amount of each engineer’s “productive” code, or code that provides business value. An engineer creating a whole new solution or implementing sweeping code changes will likely deal with lots of trial and error with a low efficiency rate. An engineer who is making a lot of small changes with a low churn rate, however, is likely to have a higher efficiency rate.
  5. Cycle time — Cycle time, part of lead time, is how long it takes to make a desired change to the software and put it into production. When a team is using DevOps and employing continuous integration, continuous delivery (CI/CD) practices, they can often measure cycle time in minutes rather than months.
  6. Open and close rates — The open and close rate is the number of issues or problems developers report and fix or close over a period of time.
  7. Production — Production is a downtime analysis and includes mean time between failures (MTBF) and mean time to recover/repair (MTTR). These measure how well software performs in a production environment.

TEAL to the Rescue

What does improving software engineering efficiency and productivity look like in a real-life situation? The professionals at Parkar Consulting & Labs used the TEAL test automation solution to solve a problem in the Kronos development process. Kronos is a workforce management software and services company whose leaders needed to test a large set of applications on various frameworks. With such large loads, they needed a more efficient way to test their products. The Parkar experts created a flexible, scalable test solution that was 100% automated as well as reduced defect leakage by 85% and execution time by 90%. The single test solution removes the need for other tool sets, reducing management headaches and enhancing the user experience.

But which metrics are the most important for helping you meet your goals?

Making use of metrics

With so many metrics available to track, it’s important developers don’t get carried away. Only track metrics directly tied to your business goals. Typically, you’ll want to use metrics to judge general trends to see where problems might exist, then do more in-depth research to discover more specific causes for inefficiencies or low productivity. For example, a high churn rate could mean a problem with the development process, such as lack of clarity over requirements or an issue with a specific engineer who might not have the best skills for developing a particular set of code. An engineer with a low churn rate, however, may have developed highly efficient code, or the low churn could indicate that he or she already resolved major issues.

Metrics are great to show the management team, but those who don’t know how to interpret or use them to drive improvements might as well not track them at all. If productivity is not up to expectations or efficiency is lagging, it might be time to take a hard look at the above seven metrics to decipher where problems exist. Developers can then use this information to make positive business changes. Once they understand their strengths and opportunities for improvement, software developers can take action. When they increase software development efficiency and productivity, they can increase product ROI, improve the development process, reduce costs, and better manage workloads.

Ready to make your software development and testing processes more efficient?  Contact the professionals at Parkar Consulting & Labs today.

DevOps: Unify Your Software Development and Operations Teams

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You’re involved in software development, but are you stuck in the past? If your development and operations professionals use older models, they likely work in two separate teams with little integration or interaction until development hands projects off to operations. Without constant communication, your operations teams test products and send changes back to development teams to incorporate. That’s a lot of information to pass back and forth — not to mention a lot of rework. It’s not only difficult to estimate time and costs while utilizing this method but goals and technologies can also change during the project cycle.

If you haven’t heard of DevOps, your product development process likely sounds a lot like the example above — and it might be costing you more time and frustration than necessary. But other than being a software development buzzword, do you know what it is and what it means — or could mean — to your project management processes? If not, it’s time you found out.

What is DevOps?

DevOps, a portmanteau of development and operations, is an organizational structure and culture with a continuous development and testing cycle. That means a lot less back and forth between development and operations teams as well as less rework, making for a quicker time to market. In fact, DevOps projects allow developers to produce fully functional, high-quality final products more quickly and cost-efficiently than other development processes. This is made possible through the DevOps continuous integration, continuous delivery (CI/CD) project management model that makes extensive use of automation.

CI/CD practices

In a DevOps organization, development team members work closely with operations personnel throughout project development from the initial build through release. CI allows two or more different developers to write code, which they continually integrate with code from other developers — at least daily — in an area known as the source repository. With the code in a single location, developers can then test it. Code will behave differently in different environments, so if developers continually test in different environments, they can be fairly certain it will perform as expected in most situations. Developers usually use a tool such as a CI server for the coding, integration, and testing so each piece of code and all changes are implemented, tested, and verified before moving on.

This leads to the CD component of CI/CD. Through close collaboration between the development and operations teams, developers can discover and correct problems earlier in the development process. In a CD environment, developers not only integrate and test code after they make changes but they also test in environments similar to production. This helps ensure their code works as intended in the real world and reduces product failures and rollbacks.

DevOps model advantages

DevOps streamlines the development process through such cross-team collaboration and a circular approach. Instead of development team members fighting to release new features and operations team members worrying about new code stability, both teams work together to deliver new features and stabilize code at the same time. Through a shared code base, continuous integration, and constant testing, they can discover problems and fix them earlier in the development process.

In non-DevOps environments, developers write all code then hand it off to the operations team in a slow, cumbersome linear process that often results in building new features on top of poor or untested base code. In some cases, development teams need to scrap entire projects and restart using alternative development methods.

With DevOps, you can achieve benefits such as:

  • Shorter time to market
  • Greater agility in responding to problems or changing specifications
  • More frequent product update delivery
  • Greater collaboration across your development and operations teams, resulting in better end products with greater usability
  • Fewer deployment problems and failures
  • Greater alignment between IT and other areas of your business

Some benefits are intangible, such as greater collaboration, communication, and trust among team your members, which can lead to more brainstorming and innovation. Working within the DevOps model, your development and operations team members can cultivate more confidence, empowerment, and job satisfaction.

Fast forward into the future! If you’re ready to help those in your organization experience the benefits of a DevOps life cycle management environment, it’s important you work with an expert team to coordinate the process and reap the most value.

The team at Parkar Consulting & Labs has streamlined product life cycle development for multiple clients using DevOps CI/CD best practices — including reducing the incumbent cost by a fifth and time to deployment by half for one client. Ready to improve your product management process with DevOps? The experts at Parkar Consulting & Labs are ready to help you get there. Contact us today.

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