In today's fast-evolving technological landscape, software development teams strive tirelessly to create innovative applications catering to diverse user needs. An integral aspect of such endeavors lies in the art of 'feature elicitation,' where developers seek inspirational sources - traditionally through exploring competitor offerings in mobile application stores like Apple's infamous "app store." However, as generative artificial intelligence (AI) progressively dominates modern discourse, one may wonder how these groundbreaking models might reshape traditional practices. This intriguing query led Jialiang Wei et al., researchers at EuroMov Digital Health in Motion, IMT Mines Alès, University Montpellier, among other institutions, to conduct an exploratory investigation comparing conventional app exploration strategies against those relying upon state-of-the-art large language models (LLMs). Their findings, published under the arXiv banner, illuminate captivating similarities alongside notable distinctions deserving careful examination.
The research team embarked upon a comprehensive comparison exercise involving two distinct methods of generating software sub-feature recommendations. The initial approach relied heavily on emulating real-world scenarios observed within typical app marketplace environments; hereafter referred to as the 'App Store Strategy.' Conversely, they explored the alternative pathway harnessing advanced LLMs to generate creative insights - christened the 'Large Language Model (LLM)-Based Strategy.' Meticulously examining a substantial dataset comprising 1,200 handcrafted suggestions sourced via either route, the investigators delved deep into the strengths, weaknesses, and unique characteristics inherent in each paradigm.
Both the analyzed methodologies displayed remarkable prowess in recommending highly pertinent sub-features accompanied by concisely articulated explanations. Nonetheless, the research unequivocally highlighted the LLM-driven strategy's propensity towards excelling in handling previously untapped, unfamiliar domains - a critical differentiation setting it apart from the App Store Strategy rooted firmly in existing solutions' examination. Although striking gold mines in terms of innovation, the LLM approach wasn't devoid of certain caveats. Occasionally, proposed functionalities appeared fantastical in nature, raising concerns over practical implementability. These instances underscore the indispensable need for a seasoned human analyst's presence throughout the entire elicitation cycle, balancing creativity with pragmatism.
As technology continues evolving exponentially, the world of software development demands constant reinvention. Insights gleaned from Wei et al.'s enlightening comparative study serve as a testament to the transformative power of integrating AI-backed mechanisms, specifically LLMs, into mainstream processes like requirement gathering. Embracing this symbiosis could potentially revolutionize product design, instilling a fresh impetus into the ever-competitive race toward creating cutting edge digital experiences.
References: Wei, Jialiang, Anne-Lise Courbis, Thomas Lambolais, Binbin Xu, Pierre Louis Bernard, Gerard Dray, Walid Maalej, ... (2024). Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach. Retrieved September 3rd, 2024, from http://arxiv.org/abs/2408.17404v1
Source arXiv: http://arxiv.org/abs/2408.17404v1