| 1. |
- Bjarnason, Elizabeth, et al.
(author)
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Prototyping Practices in Software Startups : Initial Case Study Results
- 2021
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In: Proceedings - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021. - 2332-6441 .- 1090-705X. - 9781665418980 ; 2021-September, s. 206-211
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Conference paper (peer-reviewed)abstract
- Software startups use prototyping to develop and test business ideas and to validate market viability. While prototyping is emphasized in agile methods, there is little research on how startups can best utilise scarce resources to effectively use prototypes in their dynamic business context. We performed a case study of four startups and investigated how startups currently use prototyping to elicit, validate and communicate requirements through semi-structured interviews. Our initial results indicate that prototyping is a commonly applied practice that is implicitly required to obtain funding for early stage startups, that software engineering competence is required to produce interactive and fully functioning prototypes, and that it is a challenge to balance prototype scope against expected gains.
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| 2. |
- Fischbach, Jannik, et al.
(author)
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Fine-Grained Causality Extraction from Natural Language Requirements Using Recursive Neural Tensor Networks
- 2021
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In: Proceedings of the IEEE International Conference on Requirements Engineering. - : IEEE Computer Society. - 9781665418980 ; , s. 60-69
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Conference paper (peer-reviewed)abstract
- [Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to extract causal relations from natural language requirements in fine-grained form. Specifically, existing approaches do not consider the combinatorics between causes and effects. They also do not allow to split causes and effects into more granular text fragments (e.g., variable and condition), making the extracted relations unsuitable for automatic test case derivation. [Objective Contributions:] We address this research gap and make the following contributions: First, we present the Causality Treebank, which is the first corpus of fully labeled binary parse trees representing the composition of 1,571 causal requirements. Second, we propose a fine-grained causality extractor based on Recursive Neural Tensor Networks. Our approach is capable of recovering the composition of causal statements written in natural language and achieves a F1 score of 74% in the evaluation on the Causality Treebank. Third, we disclose our open data sets as well as our code to foster the discourse on the automatic extraction of causality in the RE community. © 2021 IEEE.
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| 3. |
- Henao, Pablo Restrepo, et al.
(author)
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Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews
- 2021
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In: Proceedings of the IEEE International Conference on Requirements Engineering. - : IEEE Computer Society. - 9781665418980 ; , s. 80-86
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Conference paper (peer-reviewed)abstract
- Identifying feature requests and bug reports in user comments holds great potential for development teams. However, automated mining of RE-related information from social media and app stores is challenging since (1) about 70% of user comments contain noisy, irrelevant information, (2) the amount of user comments grows daily making manual analysis unfeasible, and (3) user comments are written in different languages. Existing approaches build on traditional machine learning (ML) and deep learning (DL), but fail to detect feature requests and bug reports with high Recall and acceptable Precision which is necessary for this task. In this paper, we investigate the potential of transfer learning (TL) for the classification of user comments. Specifically, we train both monolingual and multilingual BERT models and compare the performance with state-of-the-art methods. We found that monolingual BERT models outperform existing baseline methods in the classification of English App Reviews as well as English and Italian Tweets. However, we also observed that the application of heavyweight TL models does not necessarily lead to better performance. In fact, our multilingual BERT models perform worse than traditional ML methods. © 2021 IEEE.
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| 4. |
- Ismaeel, Khaled, et al.
(author)
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Security Requirements as Code : Example from VeriDevOps Project
- 2021
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In: 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW). - 9781665418980 ; , s. 357-363
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Conference paper (peer-reviewed)abstract
- This position paper presents and illustrates the concept of security requirements as code – a novel approach to security requirements specification. The aspiration to minimize code duplication and maximize its reuse has always been driving the evolution of software development approaches. Object-Oriented programming (OOP) takes these approaches to the state in which the resulting code conceptually maps to the problem that the code is supposed to solve. People nowadays start learning to program in the primary school. On the other hand, requirements engineers still heavily rely on natural language based techniques to specify requirements. The key idea of this paper is: artifacts produced by the requirements process should be treated as input to the regular object-oriented analysis. Therefore, the contribution of this paper is the presentation of the major concepts for the security requirements as the code method that is illustrated with a real industry example from the VeriDevOps project.
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| 5. |
- Jadallah, Noah, et al.
(author)
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CATE : CAusality Tree Extractor from Natural Language Requirements
- 2021
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In: Proceedings of the IEEE International Conference on Requirements Engineering. - : IEEE Computer Society. - 9781665418980 ; , s. 77-79
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Conference paper (peer-reviewed)abstract
- Causal relations (If A, then B) are prevalent in requirements artifacts. Automatically extracting causal relations from requirements holds great potential for various RE activities (e.g., automatic derivation of suitable test cases). However, we lack an approach capable of extracting causal relations from natural language with reasonable performance. In this paper, we present our tool CATE (CAusality Tree Extractor), which is able to parse the composition of a causal relation as a tree structure. CATE does not only provide an overview of causes and effects in a sentence, but also reveals their semantic coherence by translating the causal relation into a binary tree. We encourage fellow researchers and practitioners to use CATE at https://causalitytreeextractor.com/ © 2021 IEEE.
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