ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information

Requirements elicitation requires extensive knowledge and deep understanding of the problem domain where the final system will be situated. However, in many software development projects, analysts are required to elicit the requirements from an unfamiliar domain, which often causes communication barriers between analysts and stakeholders. In this paper, we propose a requirements ELICitation Aid tool (ELICA) to help analysts better understand the target application domain by dynamic extraction and labeling of requirements-relevant knowledge. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural language processing tasks. In addition to the information conveyed through text, ELICA captures and processes non-linguistic information about the intention of speakers such as their confidence level, analytical tone, and emotions. The extracted information is made available to the analysts as a set of labeled snippets with highlighted relevant terms which can also be exported as an artifact of the Requirements Engineering (RE) process. The application and usefulness of ELICA are demonstrated through a case study. This study shows how pre-existing relevant information about the application domain and the information captured during an elicitation meeting, such as the conversation and stakeholders' intentions, can be captured and used to support analysts achieving their tasks.Comment: 2018 IEEE 26th International Requirements Engineering Conference Workshop

What Works Better? A Study of Classifying Requirements

Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.Comment: 7 pages, the 25th IEEE International Conference on Requirements Engineering (RE'17

Two Sides of the Same Coin: Software Developers' Perceptions of Task Switching and Task Interruption

In the constantly evolving world of software development, switching back and forth between tasks has become the norm. While task switching often allows developers to perform tasks effectively and may increase creativity via the flexible pathway, there are also consequences to frequent task-switching. For high-momentum tasks like software development, "flow", the highly productive state of concentration, is paramount. Each switch distracts the developers' flow, requiring them to switch mental state and an additional immersion period to get back into the flow. However, the wasted time due to time fragmentation caused by task switching is largely invisible and unnoticed by developers and managers. We conducted a survey with 141 software developers to investigate their perceptions of differences between task switching and task interruption and to explore whether they perceive task switchings as disruptive as interruptions. We found that practitioners perceive considerable similarities between the disruptiveness of task switching (either planned or unplanned) and random interruptions. The high level of cognitive cost and low performance are the main consequences of task switching articulated by our respondents. Our findings broaden the understanding of flow change among software practitioners in terms of the characteristics and categories of disruptive switches as well as the consequences of interruptions caused by daily stand-up meetings