Generative AI software engineering
How can Generative AI improve software engineering in your organisation?
Software developers aren’t going anywhere, but the latest AI tools could drastically improve the way they work. In this guide, we’ll break down the possibilities, benefits, and risks of generative AI in software engineering.
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Why Gen AI
Why is Generative AI for software development a hot topic?
It’s official: we’re now living in the AI age. Since the launch of Chat GPT-3 in 2022, the world has woken up to the possibilities of this blossoming technology. Businesses are quickly integrating AI into their products—or building new products around it—in virtually every industry.
But while ‘narrow’ AI systems (like those used to make product or movie recommendations) have fairly limited applications, generative AI is getting people really excited. Generative AI harnesses models based on billions of data points to generate completely new information like text, songs, images, or code.
Gen AI and Software developers
How does AI generate code for software developers?
Prompt
The user provides an input or prompt, which could be a partial code snippet, a function signature, or a natural language description of the desired functionality.
Predict
The AI will then ‘predict’ the next code token iteratively until the desired code segment is completed.
Produce
Thanks to large language models, AI can use syntax and programming conventions to ensure its code is syntactically correct and relevant to the prompt.
This innovation is now opening the door for businesses to use AI in a highly technical and creative field: software engineering.
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AI coding tools
Are AI coding tools going to replace software engineers?
Let’s get one thing out of the way first: AI is not going to replace software developers any time soon.
However, businesses are already launching products that can produce code, and are arguably capable of acting as assistants to developers.
Available code generation products for software development include:
Chat-GPT
Auto-GPT
GitHub Copilot
Tabnine
Google Gemini
Amazon CodeWhisperer
Cursor
These ‘off-the-shelf’ AI coding tools have been trained on existing code—for instance, GitHubCopilot has been trained on open source code from Github, which Microsoft owns. AI developer tools will work in a similar way to Chat-GPT, allowing the users to enter a ‘prompt’ explaining the code they need. They will then deliver code, functions, and code blocks based on the data they have been modelled on.
Of course, this is a rapidly evolving space that businesses are investing in heavily. It’s likely that more companies will emerge shortly in the race to dominate the software engineering market.
AI Software Engineering Tasks
What kind of tasks can generative AI handle in software engineering?
Anyone with experience in this field understands that software development isn’t simply a matter of understanding formulas — it’s a creative process learned through practice.
So while AI isn’t anywhere near replicating what a senior software developer can do, it can help to some extent with:
Writing new code
Developers can use AI to generate boilerplate code and templates, including standard structures, configurations, and initial implementations. Generative AI can also suggest ways to complete partial code snippets, functions, or larger code blocks based on what a developer has already written.
Even more impressively, generative AI can also ‘translate’ instructions from plain English into code. A developer can describe a function, and the AI model will create code for it.
Debugging existing code
Generative AI can quickly identify coding mistakes, such as syntax errors, runtime errors, and logical errors. It can also use pattern recognition to detect anomalies and deviations that can lead to bugs.
Suggesting ways to re-factor or optimise existing code
If trained well on coding standards, generative AI can identify inconsistencies, vulnerabilities, and opportunities to reduce complexity in the code. It can provide qualitative and quantitative insights into code quality and generate explanatory comments for the development team.
Reverse-engineering legacy systems
For development teams, maintaining or upgrading code that someone else wrote can be a mammoth task. Generative AI can perform static and dynamic analyses to identify functions, runtime interactions, and much more.
It can also create documentation for the codebase, mapping out inter-module relationships and API dependencies.
Testing code
Every piece of software needs testing, and the more complex the program, the longer it takes. Generative AI can shortcut the process by generating test cases automatically, taking away repetitive and time-consuming tasks.
It can also simulate user workflows and upgrade regression test suites automatically as developers evolve the codebase.
While all of these processes will still require human involvement, AI allows developers to speed up repetitive and time-consuming tasks. This ultimately means developers spend less time troubleshooting and more time adding value.
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Gen AI Benefits
What are the benefits of using generative AI in software development?
Generative AI will, to some degree, accelerate or replicate the work of software developers. This will have numerous benefits:
Improve productivity and cost-savings
Agile processes are highly collaborative, but if you’re hiring off-shore teams, they won’t work closely with you. Instead, they’ll typically follow briefs you send them. Off-shore teams often end up ‘converting’ the project to a waterfall format, meaning you lose the benefits offered by agile.
Achieve greater flexibility in development
UX agencies will often create prototypes and use these as the starting point for their work. Applying this process to software development can work well for simple apps and smaller companies. However, it doesn’t work well for enterprise software due to the complex interdependencies involved.
Enhance team capabilities across the software development lifecycle
Enterprises often have to harness huge amounts of secure data. New technologies are emerging all the time, and enterprises will need to integrate their data with them in order to provide modern services.
Onboard new developers rapidly
With assistance from generative AI tools, new developers can get familiar with the codebase and begin their tasks without depending on senior developers.
Give organisations more control over who maintains their legacy codebase
Organisations can sometimes feel ‘locked in’ to their relationship with the developers who originally built their software. But where AI can decode the legacy codebase, the organisation will have more freedom to work with different developers, both internally and externally.
Make it easier to acquire software-related intellectual property
acquisition is complex, and the success of a handover may be dependent on the acquirer retaining certain developers. Where generative AI can codify legacy knowledge, companies can more easily hand over a new software acquisition to their own development team.
Collaborate with offshore teams more efficiently
Senior offshore engineers and product owners often need to hand over tasks to developers in different time zones. With help from a generative AI assistant that understands the context of the work, offshore developers can get helpful guidance while the on-shore team sleeps.
Gen AI Software Engineering Risks
What are the risks of using generative AI in software engineering?
Generative AI is impressive, but it’s far from being able to replicate all the work of a skilled developer—or an experienced, multi-disciplinary team.
Many of the drawbacks of generative AI revolve around the way it’s trained. AI models are arguably only as effective as the code they are trained on, and cannot consider the project’s objectives in the same way a human can.
This leads to a number of risks:
Code quality risks
Generative AI technology does a lot right and can even be effective in debugging code. However, AI-generated code frequently contains flaws. Companies using generative AI to develop code will still need checks and corrections from a senior developer.
Some experts believe that future generative AI innovations will provide clean, human-level code. But for now, businesses adopting the technology need to be wary of code quality issues.
Security and privacy risks
Generative AI systems are modelled on many thousands of existing components, formulas, and codebases. This leaves some uncertainty over security, because it's possible that the systems may have modelled compromised or outdated code.
Additionally, these systems might be linked to characters or libraries that have security risks. These uncertainties around security mean that ‘off-the-shelf’ AI systems may not meet the data security requirements of some sectors.
Copyright and intellectual property complications
Because generative AI is modelled on existing code, there is a change that it could replicate intellectual property that belongs to someone else.
Even if a company like Microsoft claims its generative AI systems were developed only using open-source code, organisations using the systems cannot be sure. This presents legal complications and risks that are too large for some organisations to take.
Prohibitive costs of AI modelling
Some companies may attempt to get around the aforementioned security and intellectual property hurdles by building their own generative AI models.
However, the amount of data needed to conduct such modelling is staggering—and consequently, the costs reach hundreds of millions, or even billions of dollars. Accordingly, only the largest and richest companies have the resources to build their own Generative AI models.
Gen AI Challenges
What other challenges should you expect when starting to leverage generative AI in your business?
Rapid evolution of generative AI solutions
Multiple companies are now attempting to stake their space in the AI landscape, with new entrants emerging all the time. The technology is so new that it’s difficult to get an objective view of how reliable any given product is.
Internal resistance to change
Using generative AI technology represents a major shift for any development team, and it’s inevitable that some professionals will be less than fully cooperative. Some team members may require extensive demonstrations and training before they are convinced of an AI tool’s effectiveness.
Technical capabilities and requirements
AI systems require a lot of processing power and storage, which potentially represents additional costs for companies deploying these tools. What’s more, companies may have to ensure the tools are compliant with data privacy requirements.
Team morale considerations
If generative AI can provide sufficient shortcuts for development teams, this could lead to some team members losing their jobs. While this may provide cost savings for the company, it could heavily impact team morale and stability.
Development of governance standards
Generative AI has amazing potential, but using AI will need to create their own standards around risk management, quality assurance, and organisational alignment. This will not only ensure the company gets positive outcomes in the software it develops, but also help its teams to build trust in the tools.
Implementing Gen AI
Get expert help implementing Generative AI into your software development processes
Discover the four-step process we use to bring the power of AI into your organisation
As one of the UK’s leading enterprise software developers, we’ve helped numerous businesses integrate generative AI technology in their tech stacks.
But when it comes to actually using artificial intelligence, we don’t just talk about it. We already use automation in our own development processes, successfully accelerating coding while maintaining superior code quality.
Our consultants help you implement generative AI in software development by following a four-step process:
Understand your development process
We start by examining your current team, processes, and development practices. This allows us to determine what benefits generative AI could potentially bring your team, and how feasible it would be to integrate it.
Find the ideal AI coding tools
The landscape of AI coding tools is changing by the day, so choosing the right one can be challenging. Using first-hand experience of deploying AI, we help you determine the most appropriate tools that fit your business needs and technological capabilities.
Set up your systems
We work with your team to integrate your new tool(s) into your tech stack. Next, we create a proof of concept to test that your AI produces the output you need.
Train your staff
Like any tool, generative AI has a learning curve. We give you a full rundown of your AI’s limitations and capabilities, and train your team in prompt engineering to ensure they get optimal results from the tools.
Who are we?
Why choose Griffiths Waite for Gen AI Software Development?
Griffiths Waite is one of the UK’s leading enterprise software developers. With over 30 years’ experience in software development and maintenance, we understand the technical challenges that large organisations face.
Fully on-shore
All of our employees are based in the UK and we don’t outsource to external developers.
Collaboration focused
Meet with us online or face-to-face at our headquarters in Birmingham, England.
Highly experienced
Work with a partner that’s delivered dozens of successful consultancy projects.
Leverage generative AI in your organisation
Expert consultation and tool setup from a leading UK software developer.
AI Case Studies
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FAQs
What challenges and risks are associated with the adoption of generative AI in corporate environments?
The challenges and risks in using generative AI in corporate environments include:
Training staff to use AI tools
Performing quality checks
Avoiding copyright and intellectual property violation
Complying with security and data privacy regulations
Overcoming internal resistance
Building trust in AI tools amongst team members
How does the effectiveness of generative AI tools in software development depend on the skills and experience of the developers?
Current generative AI is ideally used as a tool, not a replacement for developers. Accordingly, developers will need to understand AI’s limitations and strengths so they can give it prompts that generate helpful outputs. Developers will also need to review any code generated by AI and make corrections where needed.
In essence, developers with a lower skill level may not make the best judgements around when and how to use AI. What’s more, they might not pick up on issues around functionality, security, and performance of the AI’s output.
Lastly, developers need to integrate AI tools into their workflows and create governance around how their team will manage AI-generated code. Completing these tasks effectively will require skills and experience that a more junior developer may not possess.
How can organisations overcome scepticism and build trust in the use of generative AI?
Generative AI has enormous potential, but it also represents a huge change in the industry. To overcome internal resistance or scepticism from your team, include the following steps in your AI roll-out:
1. Educate staff on the technology.
Many team members will not have used AI in their day-to-day working life before. By bringing them up to speed with the possibilities and limitations of the technology, and training them in prompt engineering, you can help them understand the value of GenAI.
2. Run proof-of-concept projects.
Break your team in slowly by using AI in small projects to begin with. Demonstrate how the tool helped improve productivity, reduce errors, or accelerate development cycles to win your colleagues over.
3. Make clear announcements about how you’ll use AI.
Share your plans for deployment openly, including how any productivity or cost savings will impact staff. Make sure team members understand your organisation’s ideas for governance and training.
4. Develop quality assurance procedures.
Above all, team members wont trust AI unless they believe it can help them. Create quality assurance procedures that help your team use AI to their advantage while managing its code generation limitations.
