AI-Augmented Software Engineering

While Artificial intelligence (AI) has invaded many industries, the IT industry is reaping the benefits of AI in software engineering practices. The traditional method of relying solely on human coders throughout the entire development lifecycle is gradually becoming obsolete. 

Instead, AI-augmented software engineering has come into the arena to make the software engineering process faster, easier, and more reliable. 

This article explains what AI-augmented software engineering is, how it differs from traditional software engineering, and the role of AI in new development methods. The article also explores the tools used and the challenges of AI-augmented software engineering. 

What is AI-augmented software engineering?

AI-augmented software engineering integrates Machine Learning (ML) and AI technologies as tools for the software development process. The combination of humans and AI not only accelerates software development by eliminating repetitive work but also enhances code reliability compared to the conventional human-only development process. For instance, AI developers leverage AI tools to: 

  • Generate code
  • Fix bugs
  • Create documentation

With the recent advancements in AI tools like ChatGPT, software engineering is driven toward leveraging these technologies to make life easier for developers and other software engineering professionals. At the same time, there is a risk of replacing human developers with AI tools and techniques in the future. 

How does it differ from traditional software engineering? 

Traditional software engineering relies heavily on manual coding and development practices. The more experienced the developer or tester, the more reliable and efficient the development and testing processes will become. 

On the other hand, AI-augmented software engineering relies on  AI tools and techniques to enhance productivity, quality, and efficiency. 

Such tools can be leveraged across various stages of the development lifecycle, from project management and planning to deployment. For instance, the traditional code review process is entirely human-based. However, in AI-augmented software engineering, code reviews are facilitated by AI-powered code-reviewing tools. These tools can access many online repositories and provide suggestions for code improvement.

How AI is used in AI-augmented software engineering

AI-augmented software engineering uses AI tools in many areas of the software engineering process. 

Creating code

This is probably the most prominent usage of AI-powered tools in software engineering. ChatGPT and other AI-assisted applications enable programmers to generate code by specifying their requirements. Such tools can create code snippets in any language of your choice. They use existing databases, code repositories, and other knowledge bases to generate code automatically. 

These tools can generate the most efficient and reliable code possible and use advanced methods or functions in particular languages. Sometimes, AI-augmented software engineering enables creating whole applications using AI tools, depending on the requirements. 

However, there are still certain limitations like identifying deprecated code. 

Bug fixing and improving code

In traditional software engineering, developers pinpoint the root cause of a bug through rounds of manual debugging. They may refer to various articles and websites or consult senior programmers to figure out the root cause of an issue.

In  AI-augmented software engineering, developers can simply copy and paste the existing code and ask AI tools to identify bugs and performance issues in the code snippets. The AI tool will then automatically…

  1. Analyze the code using existing execution traces, errors, issue reports, etc.
  2. Identify the root cause.
  3. Provide suggestions on improving the code. 

Understanding code and generating documentation

Sometimes, developers won’t understand complex coding or code written by someone else. In such cases, they can ask AI tools to simply explain the code, allowing them to fully grasp the concepts they want to learn. 

Also, AI can automatically generate code documentation, letting developers focus more on important functionalities like automating repetitive tasks to save time and improve their productivity. Software engineering also involves creating user manuals, help articles, product articles, and other documentation work. AI-augmented software engineering replaces such labor-intensive manual tasks with AI tools. 

Language translation

Engineering teams occasionally need to shift their code from one language to another. In such instances, they may employ AI tools to easily translate the syntax or code structures to the target language.

AI-powered testing

In the current enterprise landscape, automation testing is driving toward autonomous testing. There, AI tools are leveraged for quality engineering processes from test case generation to test execution. It significantly lowers human intervention, reducing human errors associated with testing processes. 

Developers also use AI tools to generate unit tests for the functions they write. AI-powered testing can continually learn from failed test cases and enhance AI models.

Automation of non-development tasks

In addition to software development and testing tasks, AI-augmented software engineering can leverage AI tools to automate various day-to-day tasks

For example, responding to emails can be automated. AI tools can generate automatic responses and send them for your review, prioritize emails, etc. Additionally, automating meeting scheduling and responses allows developers to make more efficient use of their time compared to typical manual methods.

Project management

AI-augmented software engineering leverages AI tools to improve project management tasks for developers, testers, and other stakeholders involved in the software development project. For example, AI tools can suggest proper resource allocations to fit the available timeline by analyzing the risks and other factors. 

Tools used in AI-augmented software engineering

Now let’s take a look at some actual tools to try out. 

ChatGPT and Similar conversational AI models 

While not yet recognized as an official AI tool in software development, developers and testers worldwide already utilize ChatGPT for a variety of tasks. Those tasks include code generation, code review, bug fixing, and documentation work. The user-friendly and conversational AI model of ChatGPT has made it a popular AI tool among developers. 

Microsoft Bing AI is another similar tool used by developers to get code examples and code suggestions and learn faster about anything they need. The Bing Search API provides automatic follow-up questions that help you learn anything in-depth quickly.  Google Bard is another new tool that is still in the experimental stage. Developers can use it for code generation, intelligent code suggestions, and many more tasks similar to ChatGPT. 

(Dive into what generative AI, like ChatGPT, means for cybersecurity: it's risk and reward.)

Autonomous testing tools

Automation testing tools are powerful applications that can automate the entire software testing process.  It aims to reduce the amount of human intervention and improve the accuracy and efficiency of software testing. 

These automation testing tools have capabilities like AI-based test case generation, self-healing, and predictive analysis. Appvance, Testim, testRigor, Mabl, and Functionize are a few popular autonomous testing tools used in the industry.  

AI-based bug detection and debugging tools

There are several AI-based bug detection and debugging tools available on the market. For example, DeepCode, powered by the Snyk platform, helps developers with code reviews and finds and fixes bugs in their code. This tool uses multiple AI models and finds simple to complex bugs in various programming languages, including C++, Python, and Java.

Intel ControlFlag is another tool that utilizes AI to find errors in code. This tool, trained on a large set of production-quality code, helps developers reduce the time spent debugging code and focus on creating new ideas. 

Code generation tools

AI-augmented software engineering makes use of AI-powered code-generation tools to generate well-structured, well-formatted, and error-free code. GitHub Copilot is a tool trained on billions of lines of code and can act as a pair programmer for developers. It can automatically generate code, provide suggestions, and complete it. 

Another similar tool is Tabnine. It provides code suggestions, code completion, translates natural language to code, and performs many other tasks. Some other similar tools include Sourcegraph, aixCoder, and Hugging Face

Challenges of AI-augmented software engineering 

While AI-augmented software engineering brings many benefits to organizations, it also brings many challenges for individual software professionals and organizations. 

  • Security and privacy concerns. Sometimes, developers may exchange sensitive data with AI models like ChatGPT, Google Bard, etc. For example, the sensitive configurations of the company can be included in the code examples, and such models can collect that information to improve themselves. 
  • Pricing. Tools used in AI-augmented software engineering can be expensive for many companies. Companies will have to make large investments to survive among rival companies.
  • Bias in training models and data quality. AI models are generated using much training data. They can introduce bias that can affect the accuracy of the generated data. Additionally, the AI models used in AI tools rely on many high-quality data sets.  Finding such data can be a challenging and time-consuming task. 
  • Possibility of replacing human engineers. AI models cannot replace human software engineers completely. However, with future AI advancements, there is a possibility of human software engineers being replaced, cutting down more jobs.

(Consider the various angles & ethics of AI.)

AI-augmented software

AI-augmented software engineering aims to improve the overall software engineering process by leveraging AI and ML tools from the project planning phase to the software deployment and maintenance phases. In this context, developers leverage many AI tools to improve development tasks, including code generation, bug fixing, understanding the code, testing, etc. There are many AI-powered tools on the market for software engineering, which continue to evolve. 

Nonetheless, this new direction can bring many challenges, including the risk of replacing human coders, security and privacy concerns, and requiring large capital investments to survive in the industry. 

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This posting does not necessarily represent Splunk's position, strategies or opinion.

Shanika Wickramasinghe is a software engineer by profession and a graduate in Information Technology. Her specialties are Web and Mobile Development. Shanika considers writing the best medium to learn and share her knowledge. She is passionate about everything she does, loves to travel and enjoys nature whenever she takes a break from her busy work schedule. She also writes for her Medium blog sometimes. You can connect with her on LinkedIn.