Cross-level Requirement Traceability: A Novel Approach Integrating Bag-of-Words and Word Embedding for Enhanced Similarity Functionality
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the challenge of requirement traceability, which involves identifying the inter-dependencies between requirements at different levels of abstraction . This problem is not new, as requirement traceability has been a significant challenge, especially when dealing with requirements at various levels of abstraction . The paper proposes a novel approach that automates the task of linking high-level business requirements with more technical system requirements, utilizing a combination of Bag-of-Words (BOW) model and Term Frequency-Inverse Document Frequency (TF-IDF) scoring, along with an enhanced cosine similarity function that incorporates word embeddings to overcome the limitations of traditional cosine similarity .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate a scientific hypothesis related to enhancing requirement traceability by proposing a novel approach that integrates Bag-of-Words (BOW) and word embedding techniques for improved similarity functionality . The hypothesis revolves around automating the task of linking high-level business requirements with more technical system requirements by addressing the limitations of traditional similarity functions like cosine similarity or Manhattan distance . The proposed approach focuses on incorporating word similarities when calculating document similarity to capture the interrelationships among dimensions, specifically words in the representation method, to enhance the traceability process . The study seeks to evaluate the effectiveness of this novel approach through experiments conducted on well-known datasets, demonstrating significant improvements in efficiency compared to existing methods, with notable increases in performance metrics such as the F2 score .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper proposes a novel approach that integrates Bag-of-Words (BOW) and Word Embedding to enhance similarity functionality in the context of cross-level requirement traceability . The key innovation lies in introducing a new similarity function that combines elements from both traditional Bag-of-Words (BOW) approaches and embeddings-based approaches . Instead of relying on traditional similarity functions like cosine similarity or Manhattan distance, the paper suggests a hybrid solution that aims to capture the interrelationships among dimensions, specifically words in the representation method .
One of the main ideas presented in the paper is to address the limitations of traditional similarity functions by incorporating word similarities when calculating document similarity . This proposed function takes into account a similarity matrix, which includes similarities between every pair of words, alongside the two vectors being compared . By doing so, the new function aims to capture semantic nuances accurately and overcome the issue of assigning a similarity score of zero to document pairs that do not share matching terms .
Furthermore, the paper discusses the utilization of embeddings-based methods, with BERT being a commonly used model in conjunction with neural network classifiers or leveraging similarities between requirements . The paper also explores the adjustment of sentence embeddings themselves, which holds potential for broader applications such as training machine learning models . Additionally, the proposed method aims to complement Bag-of-Words (BOW) representation with Term Frequency-Inverse Document Frequency (TF-IDF) scoring to enhance cross-level requirements traceability .
In terms of results, the paper demonstrates that the proposed approach surpasses existing state-of-the-art methods in certain datasets, showing promising results in dealing with cross-level requirements traceability . The method achieved better performance based on established criteria and outperformed traditional methods like VSM, LSI, Fine-tuned BERT, and Req2Vec in terms of recall and precision . The incorporation of semantic information in the proposed approach is highlighted as a key advantage over traditional methods, enabling a better understanding of the relationships between words and requirements . The proposed approach in the paper offers several key characteristics and advantages compared to previous methods in the context of cross-level requirement traceability .
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Incorporation of Semantic Information: One significant advantage of the proposed approach is the incorporation of semantic information to better understand the relationships between words and requirements. Unlike traditional TF-IDF methods that may lack semantic depth, the new approach captures the interrelationships among dimensions, specifically words, more effectively .
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Hybrid Solution: The paper introduces a hybrid solution that combines elements from both Information Retrieval (IR)-based approaches and embeddings-based approaches. By integrating Bag-of-Words (BOW) and Word Embedding, the proposed method aims to overcome the limitations of traditional similarity functions and enhance similarity functionality .
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Performance Improvement: Experimental results demonstrate that the proposed approach outperforms existing state-of-the-art methods in certain datasets, showing a significant improvement in F2 scores. The method achieved better performance based on established criteria and surpassed traditional methods like VSM, LSI, Fine-tuned BERT, and Req2Vec in terms of recall and precision .
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Handling Small Datasets: Unlike Latent Semantic Indexing (LSI), which may be limited when working with small datasets due to its reliance on pattern recognition, the proposed approach utilizes large amounts of data from embedding systems to capture and reflect information more effectively .
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Scalability and Flexibility: The proposed method offers scalability and flexibility by addressing various factors such as the domain of requirements and the diversity of expressions for a given term. It adapts well to different datasets, showcasing varying results across domains and types of requirements .
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Superior Document Representations: While embeddings-based approaches excel in representing words as vectors to capture semantic information, the proposed method aims to address the challenge of obtaining accurate document representations from individual word embeddings. By introducing a new similarity function that considers word similarities, the approach enhances the accuracy of document similarity calculations .
In conclusion, the proposed approach stands out for its innovative integration of Bag-of-Words and Word Embedding, the incorporation of semantic information, performance improvements, scalability, and the ability to handle diverse datasets effectively, offering a promising solution for enhancing cross-level requirement traceability .
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research papers exist in the field of requirement traceability. Noteworthy researchers in this area include Mahmoud, Niu, and Xu who proposed a semantic relatedness approach for traceability link recovery . Guo, Cheng, and Cleland-Huang focused on semantically enhanced software traceability using deep learning techniques . Wang, Shen, Huang, Yu, and Chen analyzed close relations between target artifacts to improve IR-based requirement traceability recovery . Rempel, Mader, and Kuschke worked towards feature-aware retrieval of refinement traces . Wang, Peng, Wang, Wang, and Li developed an automated hybrid approach for generating requirements trace links . Atas, Samer, and Felfernig automated the identification of type-specific dependencies between requirements . Nicholson and L.C. concentrated on issue link label recovery and prediction for open-source software . Tian, Zhang, and Lian proposed a cross-level requirement trace link update model based on bidirectional encoder representations from transformers .
The key to the solution mentioned in the paper is the development of a novel approach that integrates Bag-of-Words (BOW) and word embedding to enhance similarity functionality in requirement traceability. This approach involves representing each requirement using a BOW model combined with the Term Frequency-Inverse Document Frequency (TF-IDF) scoring function. Additionally, an enhanced cosine similarity method is suggested, leveraging recent advances in word embedding representation to address the limitations of traditional cosine similarity functions. The proposed solution aims to automate the task of linking high-level business requirements with more technical system requirements, demonstrating significant improvements in efficiency compared to existing methods .
How were the experiments in the paper designed?
The experiments in the paper were designed to compare the proposed method with existing state-of-the-art methods on three datasets: MODIS, WARC(NFR), and WARC(FRS) . The comparison involved representing requirements and determining their relatedness by assessing the similarity between them after applying a specific threshold . The results of the experiments were presented in tables showcasing the performance metrics such as recall, precision, F1 score, and F2 score for each method on the different datasets . Additionally, the paper included precision/recall curves to visually demonstrate the performance of the best methods on the three datasets .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is the WARC(NFR) and WARC(FRS) datasets . The data used in this work is publicly available, and the code is open source. The datasets can be accessed at the following links:
- For WARC(NFR) and WARC(FRS): http://sarec.nd.edu/coest/datasets.html
- For MODIS dataset: http://promise.site.uottawa.ca/SERepository/datasets-page.html .
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper's approach outperformed existing state-of-the-art methods in terms of F2 by 14.1% and 18.4% in the WARC(NFR) and MODIS datasets, respectively, showcasing the effectiveness of the proposed method . Additionally, the method achieved a "Good" level of performance in the WARC(NFR) dataset and showed promising results in the MODIS dataset . These outcomes indicate that the proposed approach successfully addresses the research questions and hypotheses posed in the study, demonstrating its efficacy in enhancing similarity functionality through a novel integration of Bag-of-Words and Word Embedding techniques .
What are the contributions of this paper?
The contributions of the paper "Cross-level Requirement Traceability: A Novel Approach Integrating Bag-of-Words and Word Embedding for Enhanced Similarity Functionality" include:
- Introducing a novel similarity function that combines Bag-of-Words (BOW) representation with Term Frequency-Inverse Document Frequency (TF-IDF) scoring to enhance requirements traceability .
- Proposing a hybrid solution that integrates elements from both Information Retrieval (IR) and embeddings-based approaches to develop a new similarity function capable of capturing interrelationships among dimensions, such as words in the representation method .
- Conducting experiments on datasets like COEST, WARC(NFR), and WARC(FRS) to evaluate the effectiveness of the proposed approach, demonstrating significant improvements in efficiency compared to existing methods, with an increase of approximately 18.4% in one dataset based on the F2 score .
What work can be continued in depth?
Further research in the field of cross-level requirements traceability can be expanded by delving deeper into the incorporation of word similarities when calculating document similarity. This involves developing a novel similarity function that considers the relationships between words to enhance the understanding of the interdependencies among dimensions . By exploring the adjustment of sentence embeddings themselves and utilizing these adjusted embeddings as features for training machine learning models, researchers can potentially broaden the applications of embedding similarities . Additionally, investigating the effectiveness of different approaches, such as Information Retrieval (IR) based methods or embeddings-based ones, in automating the task of connecting requirements can provide valuable insights into improving efficiency and accuracy in requirements traceability .