TY - GEN
T1 - Identifying Functional and Non-functional Software Requirements from User App Reviews
AU - Dave, Dev
AU - Anu, Vaibhav
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Mobile app developers are always looking for ways to use the reviews (provided by their app's users) to improve their application (e.g., adding a new functionality in the app that a user mentioned in their review). Usually, there are thousands of user reviews that are available for each mobile app and isolating software requirements manually from such as big dataset can be difficult and time-consuming. The primary objective of the current research is to automate the process of extracting functional requirements and filtering out non-requirements from user app reviews to help app developers better meet the wants and needs of their users. This paper proposes and evaluates machine learning based models to identify and classify software requirements from both, formal Software Requirements Specifications (SRS) documents and Mobile App Reviews (written by users) using machine learning (ML) algorithms combined with natural language processing (NLP) techniques. Initial evaluation of our ML-based models show that they can help classify user app reviews and software requirements as Functional Requirements (FR), Non-Functional Requirements (NFR), or Non-Requirements (NR).
AB - Mobile app developers are always looking for ways to use the reviews (provided by their app's users) to improve their application (e.g., adding a new functionality in the app that a user mentioned in their review). Usually, there are thousands of user reviews that are available for each mobile app and isolating software requirements manually from such as big dataset can be difficult and time-consuming. The primary objective of the current research is to automate the process of extracting functional requirements and filtering out non-requirements from user app reviews to help app developers better meet the wants and needs of their users. This paper proposes and evaluates machine learning based models to identify and classify software requirements from both, formal Software Requirements Specifications (SRS) documents and Mobile App Reviews (written by users) using machine learning (ML) algorithms combined with natural language processing (NLP) techniques. Initial evaluation of our ML-based models show that they can help classify user app reviews and software requirements as Functional Requirements (FR), Non-Functional Requirements (NFR), or Non-Requirements (NR).
KW - classification
KW - machine learning
KW - mining
KW - natural language processing
KW - requirements
UR - http://www.scopus.com/inward/record.url?scp=85133840756&partnerID=8YFLogxK
U2 - 10.1109/IEMTRONICS55184.2022.9795770
DO - 10.1109/IEMTRONICS55184.2022.9795770
M3 - Conference contribution
AN - SCOPUS:85133840756
T3 - 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
BT - 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
A2 - Chakrabarti, Satyajit
A2 - Paul, Rajashree
A2 - Gill, Bob
A2 - Gangopadhyay, Malay
A2 - Poddar, Sanghamitra
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2022
Y2 - 1 June 2022 through 4 June 2022
ER -