Turbocharge your software Quality Engineering with people-centric Gen AI transformation
Much has been written about the impact Generative AI (Gen AI) will have on future jobs and careers – with wild predictions suggesting that technology will replace the role of humans in many fields. But for us to optimize its deployment, the role of the human and its interaction with the tech will be pivotal. And I believe this is particularly the case in software quality engineering.
In our latest Capgemini Research Institute (CRI) Report – Turbocharging software with Gen AI: How organizations can realize the full potential of generative AI for software engineering, it is clear that organizations are already reaping multiple benefits from leveraging generative AI for their software engineering projects.
Gen AI is undoubtedly a game changer, and by leveraging the power of large language models (LLMs), it can enhance developers’ productivity, improve software quality, and accelerate time to market. In Gen AI, the software workforce has a tool to accelerate key tasks such as design, coding, migrating, testing, deploying, support and maintenance with minimal effort and a minimal learning curve.
Positive impact on job satisfaction
We are already seeing that Gen AI is having a positive impact on software professionals’ job satisfaction - 69% of senior software professionals and 55% of junior software professionals report high levels of satisfaction from using Gen AI for software. This only emphasises the importance of the role our people have in optimizing its deployment.
Meanwhile, 78% of software professionals are optimistic about Gen AI’s potential to enhance collaboration between business and technology teams. Furthermore, we expect Gen AI to play a key role in augmenting the software workforce with better experience, tools and platforms, as well as improved governance. So, any fears of displacement appear to be unfounded.
Myriad of use cases and applications
In terms of use cases, the applications of Gen AI within software engineering really are boundless. Our research reveals that coding assistance is the leading use case, but other popular applications in the software development lifecycle (SDLC) activities include test case generation, documentation, code modernization, and UX design assistance. The quality of the outputs will only ever be as good as the inputs though, which once again comes back to the importance of our human intervention, and the need for precision in the definition of user requirements.
Sogeti’s Gen AI Amplifier is a great example of where we have applied this principle to deliver a groundbreaking accelerator for software quality engineering. The Gen AI Amplifier puts expertly crafted prompts with advanced AI technologies at the heart of the development process to deliver a step-change in trust, quality, and speed throughout the development lifecycle.
These amplified test activities can turn month-long tasks into weeks, week-long tasks into days, and hour long tasks into just minutes. The productivity benefit of a single use case like this is simply staggering. Applying it in a real-world scenario will have huge implications in terms of how quality is planned and operationalized in the future.
Concerning lack of training and governance
The fast-moving nature of the technology seems to be causing issues regarding training, which in turn is leading to a potential lack of governance. Our research reveals that nearly a third of the workforce is self-training on Gen AI for software and less than 40% of employees are receiving formal training from their organizations.
This is a worrying gap as usage of generative AI without sufficient grasp of the underlying principles poses a significant functional, security, and legal risk for many organizations. Over 60% lack governance and upskilling programs for generative AI for software engineering, and of those software professionals who use generative AI, 63% use unauthorized tools.
In summary…
Gen AI is clearly a game changer, but for organizations to realize its full potential for software quality engineering they must place people at the heart of this transformation by creating a learning culture that permeates throughout the organization. By providing upskilling and cross-skilling opportunities, along with formalised training programmes can further enhance job satisfaction, and in doing so will remove any displacement concerns the workforce may have.
About the Research…
This is just a snapshot of the Report’s findings, so we’d encourage you to download the report to explore in more detail.
Turbocharging Software with Gen AI explores the benefits and opportunities that generative AI brings to software engineering. It draws on insights from a comprehensive multi-sectoral survey of 1,098 senior executives (director level and above) and 1,092 software professionals (including architects, developers, testers, and project managers) from organizations with over $1 billion in annual revenue. The report covers the major considerations for implementing generative AI in software engineering and includes in-depth qualitative insights from 20 industry leaders, professionals, and entrepreneurs.
- Mark BuenenHead of Global Quality Engineering & Testing
Mark BuenenHead of Global Quality Engineering & Testing