Baloğlu, Bora

Profile URL
https://hdl.handle.net/11147/16376
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Job Title
Email Address
Main Affiliation
01. Izmir Institute of Technology
Status
Former Staff
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
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WoS Researcher ID
CER-2474-2022

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Scholarly Output

1

Articles

1

Views / Downloads

7823/75

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

1

Scopus Citation Count

1

Patents

0

Projects

0

WoS Citations per Publication

1.00

Scopus Citations per Publication

1.00

Open Access Source

0

Supervised Theses

0

JournalCount
International Journal of Software Engineering and Knowledge Engineering1
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Scholarly Output Search Results

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Author Reputation Measurement on Question and Answer Sites by the Classification of Author-Generated Content
    (World Scientific Publishing, 2021) Sezerer, Erhan; Tenekeci, Samet; Acar, Ali; Baloğlu, Bora; Tekir, Selma
    In the field of software engineering, practitioners' share in the constructed knowledge cannot be underestimated and is mostly in the form of grey literature (GL). GL is a valuable resource though it is subjective and lacks an objective quality assurance methodology. In this paper, a quality assessment scheme is proposed for question and answer (Q&A) sites. In particular, we target stack overflow (SO) and stack exchange (SE) sites. We model the problem of author reputation measurement as a classification task on the author-provided answers. The authors' mean, median, and total answer scores are used as inputs for class labeling. State-of-the-art language models (BERT and DistilBERT) with a softmax layer on top are utilized as classifiers and compared to SVM and random baselines. Our best model achieves 63.8% accuracy in binary classification in SO design patterns tag and 71.6% accuracy in SE software engineering category. Superior performance in SE software engineering can be explained by its larger dataset size. In addition to quantitative evaluation, we provide qualitative evidence, which supports that the system's predicted reputation labels match the quality of provided answers.