Recent breakthroughs in Large Language Models (LLM), comprised of billions of parameters, have achieved the ability to unveil exceptional insight into a wide range of Natural Language Processing (NLP) tasks. The onus of the performance of these models lies in the sophistication and completeness of the input prompt. Minimizing the enhancement cycles of prompt with improvised keywords becomes critically important as it directly affects the time to market and cost of the developing solution. However, this process inevitably has a trade-off between the learning curve/proficiency of the user and completeness of the prompt, as generating such a solutions is an incremental process. In this paper, we have designed a novel solution and implemented it in the form of a plugin for Visual Studio Code IDE, which can optimize this trade-off, by learning the underlying prompt intent to enhance with keywords. This will tend to align with developers' collection of semantics while developing a secure code, ensuring parameter and local variable names, return expressions, simple pre and post-conditions. and basic control and data flow are met.

As the world takes cognizance of AI's growing role in greenhouse gas(GHG) and carbon emissions, the focus of AI research & development is shifting towards inclusion of energy efficiency as another core metric. Sustainability, a core agenda for most organizations, is also being viewed as a core non-functional requirement in software engineering. A similar effort is being undertaken to extend sustainability principles to AI-based systems with focus on energy efficient training and inference techniques. But an important question arises, does there even exist any metrics or methods which can quantify adoption of “green” practices in the life cycle of AI-based systems? There is a huge gap which exists between the growing research corpus related to sustainable practices in AI research and its adoption at an industry scale. The goal of this work is to introduce a methodology and novel metric for assessing “greenness” of any AI-based system and its development process, based on energy efficient AI research and practices. The novel metric, termed as Green AI Quotient, would be a key step towards AI practitioner's Green AI journey. Empirical validation of our approach suggest that Green AI Quotient is able to encourage adoption and raise awareness regarding sustainable practices in AI lifecycle.

We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.