AI-powered developers tools you must know about in 2021
Developers are used to seeing their work environment evolve at a quick pace. Every year brings its lot of new languages, innovative practices and cool tools. More than any other industry, software development has the highest adoption rate for innovation and its companies are increasingly aware of how critical it is to stay on top of it.
Successive waves of innovation have come and gone. The software-development industry is often at the head of them: the innovation our industry absorbs usually ripples into many products and solutions in the rest of the economy and society at a later stage.
And for the past couple of years, a new wave of innovation has been looming: that of artificial intelligence (AI).
Everybody can see clearly now how AI is—and will continue—being applied in so many corners of our industry; it is at the core of what enables so many new tools to assist developers throughout the various program lifecycle phases and through the wide range of issues they encounter. We believe in the power of those tools and that is why we launched our own solution—Ponicode.
Emerging AI tech will take us to the next level
Emerging AI technologies can help developers because—unlike other tools— can adapt to scenarios that run the gamut from probable to less probable through machine learning. Machine learning benefits from negative marginal costs, which make feeding it data so important. The performance of these emerging AI technologies is augmented through scale.
They improve over time and can even be customised to progressively adopt a developer’s coding style. We expect AI to enable more asynchronous and/or synchronous multi-site collaboration by reconciling developers’ work to bring about greater fidelity between past and present code, as well as between developers themselves.
When we talk about the impact of AI-enabled tools, we’re not talking about blindly automating everything and replacing developers through AI—we’re talking more about the human-centric, systemic use of AI assistants to control the reliability of code.
Thinking this way allows us to design systems that give us space to talk about the reason(s) for using AI that those who myopically focus on the ephemeral efficiency gains of automation tend to ignore. In this way, AI-powered automation ultimately means that while the creative and design-thinking work of developers remains in human hands, the peripheral tasks can weigh less heavily on developers’ shoulders and become processed much more efficiently.
In addition to augmenting the sense of support and the margin for maneuver of developers, this thoughtful, human-centric automation also reduces their cognitive load—thereby lowering the risk of burnout by addressing the three factors which lead to it.
In short, the future of developers’ work looks bright, with empowered employees experiencing higher productivity and greater agility.
Today we want to share AI-powered tools that are now available which we see as most beneficial to software developers—and how their approach to AI-enabled assistance makes a difference.
Here are seven tools that are available right now that can make software development faster and stronger thanks to Artificial Intelligence.
Pair Programming with GitHub Copilot
Since the release of GPT-3, OpenAI most powerful AI, the software industry was wondering how Microsoft would channel this technology to boost the software engineering world with new tools.
We are very excited to see if this VS Code extension will exten to new horizons and if their "technical preview" limited access will shortly turn into a public beta so we can rate it for good!
Autocomplete with Codota
When it comes to wondering how AI can help developers to better code, Codota decided to go full front and created a solution that literally helps in the motion of writing code.
Autocomplete already existed before, but Codota applies autocomplete to coding, and it works directly in your IDE. Their motto is write less, code more. Those four words really wrap it up pretty well. This AI addresses the need to code faster, as well as that of reducing the number of mistakes made while writing code. And on top of that, Codota uses its database of APIs to show you usage examples when you need inspiration.
You can try it for yourself with their free trial, but the enterprise version has the huge advantage of bringing you a contextualized experience where this AI learns your company’s pattern (safely) and adapts its insights accordingly.
Code review with DeepCode
Once your code is created, you are going to fear the Deepcode technology! 😉 The team at Deepcode created an AI-powered code-review tool that will bring to your attention, point by point, what it thinks could jeopardise your code: critical issues, of course, but it also underlines non-critical information that can help you to enhance the quality of your code.
If I’m not painting the clearest picture here, you can find examples based on real repos on their website and see for yourself.
Deepcode wants to provide AI-enabled recommendations that go beyond what the classic code-review tools that are currently available can do. It will not just bring syntax errors to your attention, but also bugs, such as file corruptions, API-contract violations or process-deadlock problems. On the security side, it can also spot vulnerabilities: hard-coded sensitive data, protocol insecurities or weak-cryptography algorithms.
With Deepcode, you will definitely improve your CI/CD pipeline, while enjoying a user-friendly interface to move through bugs and code-fixing.
Code Search with Sourcegraph
Sourcegraph addresses the same issue that the other tools of this list address, that of increasingly complex software development needing to have high safety and quality standards, without spending much time on them. Sourcegraph uses graph-theory science to search through your code better and faster. Why is it such a critical feature? Because exploring your code enables a smoother transfer of code from one person to another, faster development research and an easier way to retrieve mistakes and bugs. On top of this, Sourcegraph has features to make large-scale code changes in order to fix and smooth out the things that code search enables you to easily spot.
A must-have which is free for companies with 10 developers or fewer.
Secret detection with GitGuardian
Security, security, security. Never has a word been so critical to our world—or, should I say, to our digital world. And developers spend more and more time making sure they meet increasingly complex security standards to deliver safer software.
GitGuardian feels like a superhero hiding in the dark who is ready to save us from critical security flaws in our code. It acts on private internal data and publicly on Github, where it scans public repos and warns companies when it finds something. It will help you ensure that you don’t have connection strings, certificates, private keys or hard-coded login information left in your—or your employees’—code. It not only points outt mistakes but helps remediate them and even integrates them seamlessly into your CI pipeline.
You can protect your company against unauthorised access and find sensitive information that’s exposed right now. How could you not rush to get GitGuardian, if you don’t have it already? 😉
Diffblue uses AI to automatically write suites of unit tests for Java code that would otherwise take days or weeks to write manually.
Software testing is the #1 bottleneck in DevOps. It leads to regressions and lost developer productivity, slowing development velocity and reducing product quality. Diffblue aims to reduce this issue with automation. The free IntelliJ plugin can be used for interactive test writing on the developer desktop, and supports test-driven development (TDD) by quickly generating unit regression tests for utility code in bulk—so you can spend your time writing testable code, and unit tests that cover the complex, critical business logic.
Unit testing with… eh, duh… Ponicode
Yes, we are not the subtlest bloggers on earth, but we are not going to hide how proud we are of our tool. And it fits perfectly in this list, so, last but not least!
Our tool helps with yet another critical step in development: unit testing. Unit testing can be performed poorly, manipulated or even just skipped altogether, as it is often considered as a time-consuming step in the coding process.
Naturally, it means another field day for AI to assist developers and guide them to unleashing the full potential of their code.
Ponicode, on top of creating unit tests in one click to save time, uses AI to create smart input suggestions that enable you to never miss edge cases and to improve your code coverage without the pain of a time-consuming and manual unit-test-writing process.
Oh, and it’s free and unlimited for developers like you, so, better hop on our unicorn tool.
Security, speed, legacy, quality.
Those four magic words are at the core of the development of any new software or new features.
With AI-powered tools, the time we spend on the aforementioned can be cut—and the accuracy with which we can tend toward them—can increase tenfold. It is critical that our industry master AI to make the best of it. We are now, thanks in part to AI, moving away from a sluggish, manual approach to software development and turning towards an industrialised, large-scale quality standard capacity to bring new software to life.
And it would not be life-changing if those solutions were just product orientated: the real benefit to our industry is that it ultimately unburdens developers’ cognitive capacity so that developers like you can focus on creating shared value with innovative and better user experiences instead.