TY - GEN
T1 - Automating Key Phrase Extraction from Fault Logs to Support Post-Inspection Repair of Software Requirements
AU - Singh, Maninder
AU - Walia, Gursimran
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/2/25
Y1 - 2021/2/25
N2 - This research paper aims at developing an automated approach to identify fault prone requirements in a software requirement specification (SRS) document to mitigate the fault propagation to later phases where the same faults are harder to find and fix. This research work proposes an automated approach (i.e., KESRI) for the identification of "problematic areas"(i.e., faulty requirements) from fault logs generated during inspections. Our automated approach uses machine learning-based key phrase extraction (KPE) algorithms (both supervised and unsupervised) that can extract key phrases from fault logs and map them to an SRS document (using semantic analysis) to locate faulty requirements. To validate our proposed approach, an inspection study conducted at North Dakota State University (NDSU) with 41 inspectors using an industrial-strength SRS document that resulted in fault logs. When compared against human experts, our approach achieved F-measure of up to 83% in extracting the relevant key phrases using supervised KPE algorithms. In conclusion, our automated KPE and mapping approach has the potential to reduce manual overhead and assist authors during the fault-fixation post-inspection.
AB - This research paper aims at developing an automated approach to identify fault prone requirements in a software requirement specification (SRS) document to mitigate the fault propagation to later phases where the same faults are harder to find and fix. This research work proposes an automated approach (i.e., KESRI) for the identification of "problematic areas"(i.e., faulty requirements) from fault logs generated during inspections. Our automated approach uses machine learning-based key phrase extraction (KPE) algorithms (both supervised and unsupervised) that can extract key phrases from fault logs and map them to an SRS document (using semantic analysis) to locate faulty requirements. To validate our proposed approach, an inspection study conducted at North Dakota State University (NDSU) with 41 inspectors using an industrial-strength SRS document that resulted in fault logs. When compared against human experts, our approach achieved F-measure of up to 83% in extracting the relevant key phrases using supervised KPE algorithms. In conclusion, our automated KPE and mapping approach has the potential to reduce manual overhead and assist authors during the fault-fixation post-inspection.
KW - Key phrase extraction
KW - Requirement inspections
KW - Semantic similarity
KW - Software requirements
KW - Supervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85105481255&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105481255&partnerID=8YFLogxK
U2 - 10.1145/3452383.3452386
DO - 10.1145/3452383.3452386
M3 - Conference contribution
AN - SCOPUS:85105481255
T3 - ACM International Conference Proceeding Series
BT - iSOFT - Proceedings of the 14th Innovations in Software Engineering Conference (Formerly known as India Software Engineering Conference), ISEC 2021
A2 - Mohapatra, Durga Prasad
A2 - Mishra, Samaresh
A2 - Clark, Tony
A2 - Dubey, Alpana
PB - Association for Computing Machinery
T2 - 14th Innovations in Software Engineering Conference, ISEC 2021
Y2 - 25 February 2021 through 27 February 2021
ER -