Can We Design Software as We Talk?

Abstract

In the context of digital transformation, speeding up the time-to-market of high-quality software products is a big challenge. Main challenges. Software quality correlates with the success of requirements engineering (RE) sessions. RE sessions demand software analysts to collect all relevant material usually specified on written notes, flip charts, pictures, etc. Afterwards comprehensible requirements need to be specified for software implementation and testing. These activities are mostly performed manually, which causes process delays and software quality attributes like reliability, usability, comprehensibility, etc., are diminished causing software devaluation. Innovative aspects. This research idea paper proposes a framework for automating the tasks of requirements specification. The proposed framework involves computational mechanisms to enable the automatic generation of software design while requirements are discussed. The innovative aspect of this research comes from digitally transforming the software development life cycle (SDLC) where requirements are generated "on the fly" and virtual reality systems are in place. Potential to make change. The proposed framework has the potential to renovate the role of software analysts, which can experience substantial reduction of manual tasks, more efficient communication, dedication to more analytical tasks, and assurance of software quality from conception phases. This research idea paper introduces the framework for automating the task of requirements specification, and report our progress. We conclude the paper by outlining lessons learnt and future lines of work.

Requirements-Collector: Automating Requirements Specification from Elicitation Sessions and User Feedback

“…To classify transcripts and user reviews these two components leverage deep learning (unsupervised) and machine learning (supervised) strategies, respectively. To train the DL and ML models, these two components extract and pre-process text data from the original datasets of previous work concerning requirements analysis of transcripts [1] and user reviews feedback [2], [3]. Hence, they classify functional and non-functional requirements discussed during requirements elicitation sessions by leveraging machine learning or deep learning strategies.…”

“…Both components are available on our GitHub repositories 3 4 . Important, we experimented the usage of the two components on the dataset available from previous work, concerning the classification of requirements discussed in RE meetings (stored in form of audio recordings) and textual user review feedback [1], [2], [3].…”

“…RELATED WORK Various approaches have been proposed to extract requirements specifications from different written origins [10], [11], with approaches able to automatically classify user reviews information [4]. This work advances the mentioned state-ofthe-art by providing an unified solution enabling the automated classification of requirements discussed in RE meetings (stored in form of audio recordings) and textual user review feedback [1], [2], [3]. To the best of our knowledge, this is the first research tool that supports the combined software requirements elicitation from RE sessions and end-users feedback.…”