Hrg. Robert Ördög,
"Semi-automatic identification of sustainaibility concerns in textual requirements"
, 5-2020
Original Titel:
Semi-automatic identification of sustainaibility concerns in textual requirements
Sprache des Titels:
Englisch
Original Kurzfassung:
Sustainability is becoming increasingly important in today's world. Due to the fact that software
systems are used in more and more areas of our lives, the consideration of sustainability in
software systems is also of great importance. Yet, all actors involved in the creation of a software
system consider sustainability concerns to be rather secondary. Since requirements are crucial
for the development of a system, it is necessary to consider sustainability already in the
requirements phase. Furthermore, textual requirements offer a good opportunity to identify
sustainability aspects in software systems as they represent a complete description of it including
its features set and non-functional capabilities. Nevertheless, there is a lack of tools to support
requirements engineers in specifying requirements that take sustainability into account. Therefore,
this thesis presents a guideline that can be used by requirements engineers to objectively
determine sustainability concerns in requirements. This guideline was designed with a strong
leaning towards smart homes. Consequently, the guideline assists requirements engineers in this
field particularly well, but it can also be used for software systems from other areas. Additionally,
in the context of this thesis a software system was implemented which is able to detect
sustainability concerns in textual requirements semi-automatically by utilizing machine learning
algorithms. In order to determine which algorithm is best suited for the classification of text
requirements, an experiment was conducted in which a multitude of classification algorithms were
compared using model evaluation metrics. In this experiment, the algorithms were trained using
pre-classified requirements and tested using a smaller set of requirements. It has been found that
SVMs (Support Vector Machines) with balanced classes are most suitable. Furthermore, the
software system should be provided with a user interface that allows the user to confirm or revise
the classification results of the system to provide more data for the algorithm to train. Based on an
experiment to evaluate the quality of the guideline, it was found that requirements engineers with
the help of the guideline do agree with more than 80% when classifying requirements regarding
their sustainability concerns. The comparison of the results of the algorithm and the requirements
classified by requirements engineers shows that the requirements engineers classify only a few
percent better. The results show that the identification of sustainability issues in requirements is a
complex task, but due to the semi-automation it can be executed in a short time with almost the
same results as by a requirement engineer. Further research on the usage of the guideline and
the training or utilization of the machine learning algorithm is needed to prove if they would achieve
the same results with requirements from other domains.
Forschungs-