Reverse Engineering Variability from Natural Language Documents

Li, Yang; Schulze, Sandro; Saake, Gunter

doi:10.1145/3106195.3106207

Cited by 28 publications

(11 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides the field of app store analysis, the topic of requirements elicitation based on similar products has been also studied in software product line engineering. The majority of the works in the area focuses on the automatic identification of product features from existing documents-brochures or requirements-by means of natural language processing (NLP) techniques [37][38][39]. A literature review on similarity-based analysis of software applications is presented by Auch et al [40].…”

Section: Late Requirements Evolutionmentioning

confidence: 99%

How do requirements evolve during elicitation? An empirical study combining interviews and app store analysis

2022

View full text Add to dashboard Cite

Requirements are elicited from the customer and other stakeholders through an iterative process of interviews, prototyping, and other interactive sessions. Then, requirements can be further extended, based on the analysis of the features of competing products available on the market. Understanding how this process takes place can help to identify the contribution of the different elicitation phases, thereby allowing requirements analysts to better distribute their resources. In this work, we empirically study in which way requirements get transformed from initial ideas into documented needs, and then evolve based on the inspiration coming from similar products. To this end, we select 30 subjects that act as requirements analysts, and we perform interview-based elicitation sessions with a fictional customer. After the sessions, the analysts produce a first set of requirements for the system. Then, they are required to search similar products in the app stores and extend the requirements, inspired by the identified apps. The requirements documented at each step are evaluated, to assess to which extent and in which way the initial idea evolved throughout the process. Our results show that only between 30% and 38% of the requirements produced after the interviews include content that can be fully traced to initial customer’s ideas. The rest of the content is dedicated to new requirements, and up to 21% of it belongs to completely novel topics. Furthermore, up to 42% of the requirements inspired by the app stores cover additional features compared to the ones identified after the interviews. The results empirically show that requirements are not elicited in strict sense, but actually co-created through interviews, with analysts playing a crucial role in the process. In addition, we show evidence that app store-inspired elicitation can be particularly beneficial to complete the requirements.

show abstract

Section: Late Requirements Evolutionmentioning

confidence: 99%

How do requirements evolve during elicitation? An empirical study combining interviews and app store analysis

2022

View full text Add to dashboard Cite

show abstract

“…There are approaches that extract variability information from natural language documents such as functional requirements, see for example, Mefteh et al (2016) or Li et al (2020). For a representative overview of approaches using natural language techniques, we refer to Li et al (2017).…”

Section: Variability Extractionmentioning

confidence: 99%

Lightweight, semi-automatic variability extraction: a case study on scientific computing

Grebhahn¹,

Kaltenecker

Engwer

et al. 2021

Empir Software Eng

View full text Add to dashboard Cite

In scientific computing, researchers often use feature-rich software frameworks to simulate physical, chemical, and biological processes. Commonly, researchers follow a clone-and-own approach: Copying the code of an existing, similar simulation and adapting it to the new simulation scenario. In this process, a user has to select suitable artifacts (e.g., classes) from the given framework and replaces the existing artifacts from the cloned simulation. This manual process incurs substantial effort and cost as scientific frameworks are complex and provide large numbers of artifacts. To support researchers in this area, we propose a lightweight API-based analysis approach, called VORM, that recommends appropriate artifacts as possible alternatives for replacing given artifacts. Such alternative artifacts can speed up performance of the simulation or make it amenable to other use cases, without modifying the overall structure of the simulation. We evaluate the practicality of VORM—especially, as it is very lightweight but possibly imprecise—by means of a case study on the DUNE numerics framework and two simulations from the realm of physical simulations. Specifically, we compare the recommendations by VORM with recommendations by a domain expert (a developer of DUNE). VORM recommended 34 out of the 37 artifacts proposed by the expert. In addition, it recommended 2 artifacts that are applicable but have been missed by the expert and 32 artifacts not recommended by the expert, which however are still applicable in the simulation scenario with slight modifications. Diving deeper into the results, we identified an undiscovered bug and an inconsistency in DUNE, which corroborates the usefulness of VORM.

show abstract

“…In previous work, we (1) have shown that Software Requirements Specifications (SRS) are suitable artifacts to extract such information [21] and (2) proposed a technique that makes use of advanced Natural Language Processing (NLP) concepts to reliably extract features and variability from SRS [22]. So far, our technique has been only evaluated with a small set of rather artificial requirements, and thus, it is unclear whether it can cope with specialities of real-world requirements as well as scales up to a large amount of SRS documents.…”

Section: Introductionmentioning

confidence: 99%

Automated extraction of domain knowledge in practice

Schulze

Scherrebeck

et al. 2020

Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume a - Volume A

Self Cite

View full text Add to dashboard Cite

Software product line supports structured reuse of software artifacts in order to realize the maintenance and evolution of the typically large number of variants, which promotes the industrialization of software development, especially for software-intensive products. However, for a legacy system, it is non-trivial to gain information about commonalities and differences of the variants. Meanwhile, software requirements specifications as the initial artifacts can be used to achieve this information to generate a domain model. Unfortunately, manually analyzing these requirements is time-consuming and inefficient. To address this problem, we explored the usage of feature extraction techniques to automatically extract domain knowledge from requirements to assist domain engineers. In detail, we applied Doc2Vec and a clustering algorithm to process the requirements for achieving the initial feature tree. Moreover, we utilized key words/phrases extraction techniques to provide key information to domain engineers for further analyzing the extraction results. In particular, we developed a GUI to support the extraction process. The empirical evaluation indicates that most of the extracted features and terms are beneficial to improve the process of feature extraction. CCS CONCEPTS • Computing methodologies → Natural language processing; • Software and its engineering → Software product lines.

show abstract

Reverse Engineering Variability from Natural Language Documents

Cited by 28 publications

References 34 publications

How do requirements evolve during elicitation? An empirical study combining interviews and app store analysis

How do requirements evolve during elicitation? An empirical study combining interviews and app store analysis

Lightweight, semi-automatic variability extraction: a case study on scientific computing

Automated extraction of domain knowledge in practice

Contact Info

Product

Resources

About