Bridging the Gap between Academia and Industry in Machine Learning Software Defect Prediction: Thirteen Considerations
This experience paper describes thirteen considerations for implementing machine learning software defect prediction (ML SDP) in vivo. Specifically, we provide the following report on the ground of the most important observations and lessons learned gathered during a large-scale research effort and introduction of ML SDP to the system-level testing quality assurance process of one of the leading telecommunication vendors in the world — Nokia. We adhere to a holistic and logical progression based on the principles of the business analysis body of knowledge: from identifying the need and setting requirements, through designing and implementing the solution, to profitability analysis, stakeholder management, and handover. Conversely, for many years, industry adoption has not kept up the pace of academic achievements in the field, despite promising potential to improve quality and decrease the cost of software products for many companies worldwide. Therefore, discussed considerations hopefully help researchers and practitioners bridge the gaps between academia and industry.
Thu 14 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
10:30 - 12:00 | Software Testing for Specialized Systems 2Research Papers / Tool Demonstrations at Room C Chair(s): Zishuo Ding University of Waterloo | ||
10:30 12mTalk | Bridging the Gap between Academia and Industry in Machine Learning Software Defect Prediction: Thirteen Considerations Research Papers Szymon Stradowski Nokia & Wrocław University of Science and Technology, Lech Madeyski Wroclaw University of Science and Technology Link to publication DOI Pre-print Media Attached | ||
10:42 12mTalk | Identify and Update Test Cases when Production Code Changes: A Transformer-based Approach Research Papers Xing Hu Zhejiang University, Zhuang Liu Zhejiang University, Xin Xia Huawei Technologies, Zhongxin Liu Zhejiang University, Tongtong Xu Huawei, Xiaohu Yang Zhejiang University | ||
10:54 12mTalk | Revisiting and Improving Retrieval-Augmented Deep Assertion Generation Research Papers Weifeng Sun , Hongyan Li Chongqing University, Meng Yan Chongqing University, Yan Lei Chongqing University, Hongyu Zhang Chongqing University, Hongyu Zhang Chongqing University | ||
11:06 12mTalk | Provengo: A Tool Suite for Scenario Driven Model-Based Testing Tool Demonstrations Michael Bar Sinai Provengo, Achiya Elyasaf Ben-Gurion University of the Negev, Gera Weiss Ben-Gurion University of the Negev, Yeshayahu Weiss Ben-Gurion University of the Negev Pre-print File Attached | ||
11:18 12mTalk | QuraTest: Integrating Quantum Specific Features in Quantum Program Testing Research Papers Jiaming Ye Kyushu University, Shangzhou Xia Kyushu University, Fuyuan Zhang Kyushu University, Paolo Arcaini National Institute of Informatics
, Lei Ma University of Alberta, Jianjun Zhao Kyushu University, Fuyuki Ishikawa National Institute of Informatics File Attached | ||
11:30 12mTalk | QuCAT: A Combinatorial Testing Tool for Quantum Software Tool Demonstrations Xinyi Wang Simula Research Laboratory, Paolo Arcaini National Institute of Informatics
, Tao Yue Beihang University, Shaukat Ali Simula Research Laboratory and Oslo Metropolitan University Pre-print File Attached | ||
11:42 12mTalk | LEAP: Efficient and Automated Test Method for NLP SoftwareRecorded talk Research Papers Mingxuan Xiao Hohai University, Yan Xiao National University of Singapore, Hai Dong RMIT University, Shunhui Ji Hohai University, Pengcheng Zhang Hohai University Media Attached | ||