In this paper, we present our work on extracting and classifying requirements from large software engineering contracts. Typically, the process of requirements elicitation begins after a contractual agreement is signed by all participants. Our interactions with the legal compliance team in a large vendor organization reveal that business contracts can help in the identification of high-level requirements relevant to the success of software engineering projects. We posit that requirements engineering as a discipline has an even wider scope than software engineering of which it is traditionally considered to be a sub-discipline. This is because software engineering-specific requirements are but a part of the success story of any large project. The requirements that emerge from contracts are obligatory in nature, whether or not they pertain to core software development. Therefore, it is important that these are extracted and classified for the benefit of software engineers and other stakeholders responsible for a project. We discuss the results of an exploratory study and a range of experiments from the use of regular expressions to Bidirectional Encoder Representations from Transformers for automating the extraction and classification of requirements from software engineering contracts. With Bidirectional Encoder Representations from Transformers, we obtained a high f-score of greater than eighty four percent for classification of requirements.

Compliant software is a critical need of all modern businesses. Disambiguating regulations to derive requirements is therefore an important software engineering activity. Regulations however are ridden with ambiguities that make their comprehension a challenge, seemingly surmountable only by legal experts. Since legal experts' involvement in every project is expensive, approaches to automate the disambiguation need to be explored. These approaches however require a large amount of annotated data. Collecting data exclusively from experts is not a scalable and affordable solution. In this paper, we present the results of a crowd sourcing experiment to collect annotations on ambiguities in regulations from professional software engineers. We discuss an approach to automate the arduous and critical step of identifying ground truth labels by employing crowd consensus using Expectation Maximization (EM). We demonstrate that the annotations reaching a consensus match those of experts with an accuracy of 87%.

Software requirements specifications (SRSs) often lack the detail needed to make informed architectural decisions. Architects therefore either make assumptions, which can lead to incorrect decisions, or conduct additional stakeholder interviews, resulting in potential project delays. We previously observed that software architects ask Probing Questions (PQs) to gather information crucial to architectural decision-making. Our goal is to equip Business Analysts with appropriate PQs so that they can ask these questions themselves. We report a new study with over 40 experienced architects to identify reusable PQs for five areas of functionality and organize them into structured flows. These PQ-flows can be used by Business Analysts to elicit and specify architecturally relevant information. Additionally, we leverage machine learning techniques to determine when a PQ-flow is appropriate for use in a project, and to annotate individual PQs with relevant information extracted from the existing SRS. We trained and evaluated our approach on over 8,000 individual requirements from 114 requirements specifications and also conducted a pilot study to validate its usefulness.