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Speech-acts Based Analysis
Speech-acts Based Analysis for Requirements Discovery from Online Discussions (ISJ - under revision)
Online discussions about software applications and services that take place
on web-based communication platforms represent an invaluable knowledge
source for diverse software engineering tasks, including requirements elicitation.
The number of research works on developing effective tool-supported
analysis methods are rapidly increasing, as part of the so called software
analytics. Textual messages in App store reviews, tweets, online discussions
taking place in mailing lists and user forums, are processed by combining
natural language techniques to filter out irrelevant data; text mining and
machine learning algorithms to classify messages into different categories,
such as bug report and feature request.
Our research objective is to exploit a linguistic technique based on speech-acts
for the analysis of online discussions with the ultimate goal of discovering
requirements-relevant information. In this paper, we present a revised
and extended version of the speech-acts based analysis technique, which we
previously presented at CAiSE 2017, together with a detailed experimental
characterisation of its properties. Datasets used in the experimental evaluation
are taken from a widely used open source software project (161120
textual comments), as well as from an industrial project in the home energy
management domain. We make them available for experiment replication
purposes. On these datasets, our approach is able to successfully classify
messages into Feature/Enhancement and Other, with F-measure of 0.81 and
0.84 respectively. We found evidences that there is an association between
types of speech-acts and categories of issues, and that there is correlation
between some of the speech-acts and issue priority, thus motivating further
research on the exploitation of our speech-acts based analysis technique in
semi-automated multi-criteria requirements prioritisation.
A replication package containing the datasets (in Weka's ARFF format) used in the experiment
as well as the rules for speech-acts identification is available for download.
Analysis of Online Discussions in Support of Requirements Discovery (CAiSE'17)
Feedback about software applications and services that end-
users express through web-based communication platforms represents
an invaluable knowledge source for diverse software engineering tasks,
including requirements elicitation. Research work on automated analysis
of textual messages in app store reviews, open source software (OSS)
mailing-lists and user forums has been rapidly increasing in the last five
years. NLP techniques are applied to filter out irrelevant data, text mining
and automated classification techniques are then used to classify
messages into different categories, such as bug report and feature request.
Our research focuses on online discussions that take place in user
forums and OSS mailing-lists, and aims at providing automated analysis
techniques to discover contained requirements. We present
a speech-acts based analysis technique, and experimentally evaluate it
on a dataset taken from a widely used OSS project.
A zip archive containing the dataset and ARFF files for training models in Weka is available for download.