Global Decision Making Over Deep Variability in Feedback-Driven Software Development
To succeed with the development of modern software, organizations must have the agility to adapt faster to constantly evolving environments to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, business, development, and IT. However, stakeholders do not have sufficient automated support for global decision making, considering the increasing variability of the solution space, the frequent lack of explicit representation of its associated variability and decision points, and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. The Multi-Plane Models and Data (MP-MODA) framework explicitly represents and manages variability, impacts, and decision points. It enables automation and tool support in aid of a multi-criteria decision making process involving different stakeholders within a feedback-driven software development process where feedback cycles aim to reduce uncertainty. We present the conceptual structure of the framework, discuss its potential benefits, and enumerate key challenges related to tool supported automation and analysis within MP-MODA.
Wed 12 OctDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 18:00 | Technical Session 17 - SE for AIResearch Papers / Late Breaking Results / NIER Track / Tool Demonstrations at Banquet B Chair(s): Tim Menzies North Carolina State University | ||
16:00 10mVision and Emerging Results | On the Naturalness of Bytecode Instructions NIER Track | ||
16:10 20mResearch paper | A Light Bug Triage Framework for Applying Large Pre-trained Language Model Research Papers Jaehyung Lee Pohang University of Science and Technology, Pohang , Hwanjo Yu Pohang University of Science and Technology, Pohang, HanKisun Samsung Research | ||
16:30 10mVision and Emerging Results | Global Decision Making Over Deep Variability in Feedback-Driven Software Development NIER Track Jörg Kienzle McGill University, Canada, Benoit Combemale University of Rennes; Inria; IRISA, Gunter Mussbacher McGill University, Omar Alam Trent University, Francis Bordeleau École de Technologie Supérieure (ETS), Lola Burgueño University of Malaga, Gregor Engels Paderborn University, Jessie Galasso-Carbonnel Université de Montréal, Jean-Marc Jézéquel Univ Rennes - IRISA, Bettina Kemme McGill University, Canada, Sébastien Mosser McMaster University, Houari Sahraoui Université de Montréal, Maximilian Schiedermeier McGill University, Eugene Syriani Université de Montréal | ||
16:40 20mResearch paper | CARGO: AI-Guided Dependency Analysis for Migrating Monolithic Applications to Microservices ArchitectureACM SIGSOFT Distinguished Paper Award Research Papers Vikram Nitin Columbia University, Shubhi Asthana IBM Research, Baishakhi Ray Columbia University, Rahul Krishna IBM Research Pre-print | ||
17:00 10mDemonstration | Answering Software Deployment Questions via Neural Machine Reading at ScaleVirtual Tool Demonstrations Guan Jie Qiu School of Software, Shanghai Jiao Tong University, Diwei Chen School of Software, Shanghai Jiao Tong University, Shuai Zhang School of Software, Shanghai Jiao Tong University, Yitian Chai School of Software, Shanghai Jiao Tong University, Xiaodong Gu Shanghai Jiao Tong University, China, Beijun Shen School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University | ||
17:10 20mResearch paper | PRCBERT: Prompt Learning for Requirement Classification using BERT-based Pretrained Language ModelsVirtual Research Papers Xianchang Luo University of Science and Technology of China, Yinxing Xue University of Science and Technology of China, Zhenchang Xing Australian National University, Jiamou Sun Australian National University | ||
17:30 10mVision and Emerging Results | Test-Driven Multi-Task Learning with Functionally Equivalent Code Transformation for Neural Code GenerationVirtual NIER Track Xin Wang Wuhan University, Xiao Liu School of Information Technology, Deakin University, Pingyi Zhou Noah’s Ark Lab, Huawei Technologies, Qixia Liu China Mobile Communications Corporation, Jin Liu Wuhan University, Hao Wu Yunnan University, Xiaohui Cui Wuhan University | ||
17:40 10mPaper | Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code CorporaVirtual Late Breaking Results Anh T. V. Dau FPT Software AI Center, Nghi D. Q. Bui Singapore Management University, Thang Nguyen-Duc FPT Software AI Center, Hoang Thanh-Tung Vietnam National University | ||