Exploring techniques for rationale extraction from existing documents

Rogers, Eric; Gung,; Qiao, Qiao; Burge,

doi:10.1109/icse.2012.6227091

Cited by 20 publications

(18 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work described in this paper differs from [5] in several ways. First, we annotated bug reports by rationale type, not by rationale/not rationale.…”

Section: Related Workmentioning

confidence: 70%

“…Compared with using the general and domain ontologies with stemming and WordNet expansion [5], using the best 1,000 information gain on the dataset from [5] yielded an average of 3.14 % higher F-measure using lemma of unigrams. Using the filter with minimum term frequency of 3, we managed to drop the dimensionality of 5-g feature set from 254,120 to 23,869.…”

Section: Impact Of Feature Selectionmentioning

confidence: 96%

“…First, we annotated bug reports by rationale type, not by rationale/not rationale. The preprocessing described in [5] was much more limited than described here, with a very different set of classification features. Rogers et al [5] performed two distinct sets of experiments, one set that used WEKA and one that only used GATE.…”

Section: Related Workmentioning

confidence: 99%

“…Rogers et al [5] investigated techniques to extract rationale from bug reports. The work described in this paper differs from [5] in several ways.…”

Section: Related Workmentioning

confidence: 99%

“…We used a subset of WEKA's individual classifiers based on results from [5]. We also experimented with Ensemble Learning.…”

Section: Learning Algorithmsmentioning

confidence: 99%

See 4 more Smart Citations

Using Text Mining Techniques to Extract Rationale from Existing Documentation

Rogers

Qiao

Gung

et al. 2015

Design Computing and Cognition '14

Self Cite

View full text Add to dashboard Cite

Software development and maintenance require making many decisions over the lifetime of the software. The decision problems, alternative solutions, and the arguments for and against these solutions comprise the system's rationale. This information is potentially valuable as a record of the developer and maintainers' intent. Unfortunately, this information is not explicitly captured in a structured form that can be easily analyzed. Still, while rationale is not explicitly captured, that does not mean that rationale is not captured at all-decisions are documented in many ways throughout the development process. This paper tackles the issue of extracting rationale from text by describing a mechanism for using two existing tools, GATE (General Architecture for Text Engineering) and WEKA (Waikato Environment for Knowledge Analysis) to build classification models for text mining of rationale. We used this mechanism to evaluate different combinations of text features and machine learning algorithms to extract rationale from Chrome bug reports. Our results are comparable in accuracy to those obtained by human annotators.

show abstract

“…The work described in this paper differs from [5] in several ways. First, we annotated bug reports by rationale type, not by rationale/not rationale.…”

Section: Related Workmentioning

confidence: 70%

Section: Impact Of Feature Selectionmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

“…Rogers et al [5] investigated techniques to extract rationale from bug reports. The work described in this paper differs from [5] in several ways.…”

Section: Related Workmentioning

confidence: 99%

“…We used a subset of WEKA's individual classifiers based on results from [5]. We also experimented with Ensemble Learning.…”

Section: Learning Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Using Text Mining Techniques to Extract Rationale from Existing Documentation

Rogers

Qiao

Gung

et al. 2015

Design Computing and Cognition '14

Self Cite

View full text Add to dashboard Cite

show abstract

Effects of rationale awareness in online ideation crowdsourcing tasks

Xiao

2014

Asso for Info Science & Tech

View full text Add to dashboard Cite

Prior studies have shown that articulating and sharing rationales in traditional small-group activities contribute to the maintenance of common ground, members' knowledge awareness, and contribution awareness. It is likely that the importance of articulating and sharing rationales will be increasingly acknowledged in online crowdsourcing because in such a context, large-scale participation is expected with participants often not knowing each other and being flexible about their participation status (e.g., participants may join after the activity has started and leave before it completes), and thus more grounding efforts/support are expected. To better understand the role of shared rationales in online crowdsourcing, three experiments were conducted investigating whether and how rationale awareness affects the ideation crowdsourcing task and ideaevaluation crowdsourcing task based on the findings about the rationale awareness effects in small-group idea-generation activities. The results suggest that one's awareness of previous workers' rationales in the current task can slightly improve the average quality of generated ideas in an iterative approach. In addition, one's evaluation of an idea could be positively or negatively affected by the idea's rationale depending on the quality of the rationales. The results also suggest that showing previous workers' rationales in the ideation task may not be an effective approach for improving the best quality of generated ideas.

show abstract

Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE‐Arg framework

Khan

Liu

Wen

et al. 2020

J Software Evolu Process

View full text Add to dashboard Cite

Due to the pervasive use of online forums and social media, users' feedback are more accessible today and can be used within a requirements engineering context. However, such information is often fragmented, with multiple perspectives from multiple parties involved during ongoing interactions. In this paper, the authors propose a Crowdbased Requirements Engineering approach by Argumentation (CrowdRE-Arg). The framework is based on the analysis of the textual conversations found in user forums, identification of features, issues and the arguments that are in favour or opposing a given requirements statement. The analysis is to generate an argumentation model of the involved user statements, retrieve the conflicting-viewpoints, reason about the winning-arguments and present that to systems analysts to make informedrequirements decisions. For this purpose, the authors adopted a bipolar argumentation framework and a coalition-based meta-argumentation framework as well as user voting techniques. The CrowdRE-Arg approach and its algorithms are illustrated through two sample conversations threads taken from the Reddit forum. Additionally, the authors devised algorithms that can identify conflict-free features or issues based on their supporting and attacking arguments. The authors tested these machine learning algorithms on a set of 3,051 user comments, preprocessed using the content analysis technique. The results show that the proposed algorithms correctly and efficiently identify conflict-free features and issues along with their winning arguments. K E Y W O R D S argumentation, machine learning, natural language processing, new features, requirements, user forum 1 | INTRODUCTION Recently, with the increasing popularity and development of social media, the role of rationale management became more prominent for marketdriven software applications. Mainly, rationale management focuses on capturing and recording the justification why a decision was taken. 1 In software engineering literature, requirements and design rationale management have been a main research area. 2,3 Software developers of social applications make various decisions in finalizing the set of software features. 4 To justify their decisions, different alternatives are considered, and argumentation that helps in finalizing the decision constitutes the software rationale. 5 During software evolution, the rationale serves as a source for development decision making and enhancing the software system explainability. 6,7 Also, the rationale is meant to provide a sound reasoning of the chosen design of the system 8 and help in the understanding of

show abstract

Exploring techniques for rationale extraction from existing documents

Cited by 20 publications

References 14 publications

Using Text Mining Techniques to Extract Rationale from Existing Documentation

Using Text Mining Techniques to Extract Rationale from Existing Documentation

Effects of rationale awareness in online ideation crowdsourcing tasks

Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE‐Arg framework

Contact Info

Product

Resources

About