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2017 Fiscal Year Annual Research Report

Studying and development of comprehensive code review support technology for modern code review process

Research Project

Project/Area Number 16J02861
Research InstitutionNara Institute of Science and Technology

Principal Investigator

THONGTANUNAM Patanamon  奈良先端科学技術大学院大学, 情報科学研究科, 特別研究員(PD)

Project Period (FY) 2016-04-22 – 2018-03-31
KeywordsSoftware Engineering
Outline of Annual Research Achievements

This project aims to improve the Modern Code Review (MCR) process by recommending rigorous code review practices. Hence, I will develop a system that (A) recommends appropriate reviewer and (B) estimates necessary review effort. For research achievement in this year, I have discovered an important human factors that should be considered when finding appropriate reviewers. This finding will enhance the part (A) of the proposed system. Moreover, I have uncovered the review dynamics in code review discussion that can have an impact on software quality. This finding will improve the part (B) of the system. This work is a part of collaboration with researchers at Queen's University, Canada and under submission at the Springer Journal of Empirical Software Engineering and the IEEE Transaction of Software Engineering.

Research Progress Status

29年度が最終年度であるため、記入しない。

Strategy for Future Research Activity

29年度が最終年度であるため、記入しない。

  • Research Products

    (2 results)

All 2017

All Journal Article (1 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 1 results,  Acknowledgement Compliant: 1 results) Presentation (1 results) (of which Int'l Joint Research: 1 results)

  • [Journal Article] Review participation in modern code review2017

    • Author(s)
      Patanamon Thongtanunam,Shane McIntosh, Ahmed E. Hassan, Hajimu Iida
    • Journal Title

      Empirical Software Engineering

      Volume: 22 Pages: 768-817

    • DOI

      10.1007/s10664-016-9452-6

    • Peer Reviewed / Int'l Joint Research / Acknowledgement Compliant
  • [Presentation] Using HPC Resources to Exploit Big Data for Code Review Analytics2017

    • Author(s)
      Patanamon Thongtanunam
    • Organizer
      High Performance Computing Symposium
    • Place of Presentation
      Kingston, Canada
    • Year and Date
      2017-06-07 – 2017-06-07
    • Int'l Joint Research

URL: 

Published: 2018-12-17  

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