An Automated Approach for Mapping Between Software Requirements and Design Items: An Industrial Case from Turkey

Selin Karagöz*, Ayşe Tosun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


When a new request comes to the existing software, determining whether there will be reuse and determining where the new requests will be mapped in the existing design are important problems. Since this process is done manually by developers in the context we work, it depends on experience and domain knowledge, besides it is an error-prone and time-consuming process due to the human factor. The main purpose of this study is to correctly predict which new requests in the System Design Document (SDD) match which feature set in the existing software’s Software Requirement Specification (SRS) document. We consider the feature mapping problem between SDD items and SRS requirements as a multi-label multi-class classification problem. Zemberek, a Turkish natural language processing library, is used for preprocessing and feature extraction of the SRS document of the existing software and three SDD documents of different systems to which this software will be delivered. The features extracted from the SRS document are categorized under a certain number of feature topics using the LDA algorithm. The FastText algorithm and AdaBoost-based classifier ICSIBoost are used to decide which of the topics from the SRS document represents a feature in the SDD document, and the predictions are compared with manually determined topics by experts. ICSIBoost achieves quite 67% to 90% precision in topic predictions, whereas the FastText algorithm does not meet our expectations for small and imbalanced data.

Original languageEnglish
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
Publication statusPublished - 2022

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Feature mapping
  • Multi-label multi-class classification
  • Software product line
  • Topic modeling
  • Turkish NLP


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