IIIT Hyderabad Hosts Roundtable on Generative AI Coding
IIIT Hyderabad recently held a round-table discussion, where conversation centered specifically on the development phase of the Software Development Life Cycle (SDLC), whose deterministic nature makes it a low-hanging fruit for leveraging Generative AI effectively.
Moderated by IIITH Prof. Raghu Reddy, the brainstorming session represented a balanced mix of researchers with the industry-facing perspectives of tech product and SaaS companies(Ozonetel), Banks, (JPMC, Lloyds, DBS), THub, startups (MontyCloud), service companies (Bosch), healthcare (AIG, Evernorth) and open source (Swecha, TechVedika).
The White Paper that evolved out of the confabulation presents a holistic overview of the current scenario, future potential of Generative AI tools in software development, and offers actionable insights for practitioners and researchers.
Current use of Generative AI tools in development phase of the SDLC
Panelist concurred that LLMs and other Gen AI tools provide relatively higher efficiency compared to traditional search engines and question-and-answer (Q&A) platforms. Development practices have changed, with developers now using Gen AI tools to complete tasks in 1-2 searches against the 5-6 searches on Stack Overflow.
Challenges and workarounds
While Gen AI tools offer transformative potential in software development, panelist shared challenges they faced in contextual limitations, privacy concerns, integration complexities and organizational resistance.
Alternatives to search engines: While Gen AI tools seamlessly replace search engines and Q&A platforms, the final code has to be copied and modified to be integrated into the system.
Challenges with existing products and legacy code: The difficulty of Gen AI tools increases with existing products. For example, for language migrations, the tools still pose a challenge for converting bitwise operations to Java code.
Gaps in context and domain knowledge: Organizations who have explored multiple tools like Cursor, Copilot, Claude observed that AI tools sometimes lack the contextual awareness required for intricate coding tasks. Business specific responses are currently not a strong suit of Gen AI tools.
Cost and Training challenges: Integration into existing systems often demands substantial time and resources, as well as a commitment to restructuring workflows to accommodate AI-driven methodologies. The cost of manpower training to use un-ambiguous English in the prompts was also raised.
Creating solutions and drawing blueprints
While some founders dumped it on their engineers to “make things work”, others stated that over time, the diminishing ability of developers was a concern. However, some argued that particular skills would inevitably depreciate over the years, like in the case of Java.
To address concerns of privacy, protection and regulation of intellectual property, panelists pointed out the need for robust governance frameworks and transparency in AI tool usage. While tracking change summaries remains a challenge, ownership and accountability of the code generated and tested, lies with the code reviewers, usually a senior developer, assisted by specific models to ensure that reliable code gets accepted into the system. “Trust but Verify” was a strong undertone of the discussion.
