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Asian Software Quality Network Conference 2019
Date: Tuesday 10th Sep,2019
Venue: Toyo University, Tokyo, Japan
Program：
Time Contents
13:00-13:15
Opening
Makoto Nonaka， Toyo University， Japan
13:15-13:45
The Trend of China's Software Industry Development
Yang Genxing
East China University of Science and Technology, China
13:45-14:15
Suggestion of Testing Method for Industrial Level
Cyber-Physical System
Sunil Chon
ThinkforBL , Korea
14:15-14:30 Break
14:30-15:00
Consideration for SW quality Factor in the era of industry
4.0（ Focusing on CPS operational environment）
Yong Gyu Kim
Apexsoft , Korea
15:00-15:30
Quantifying Confidence Using Metrics
– an IV&V Perspective
Amiruddin Jaafar Sidek
Malaysian Software Testing Board , Malaysia
15:30-16:00
Software Process Metrics
Halizawati MOHD HALID
Custommedia Sdn Bhd, Malaysia
16:00-16:15 Break
16:15-16:45
Guidelines of Quality Assurance for Artificial-Intelligence-
based products and services（ QA4AI）
Yasuharu Nishi
The university of Electro-Communications, Japan
16:45-17:00
Closing
Makoto Nonaka， Toyo University， Japan
17:10-18:15
Networking
talks with drinks and snacks
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ASQN2019 main session details
・ Title: Suggestion of Testing Method for
Industrial Level Cyber-Physical System
・ Name: Sunil, Chon
・ Affiliate: ThinkforBL Consultant
・ Bio: Consulting over 100 R&D companies. Over 40
patents related to testing, SW engineering and safety
Abstract: As CPS becomes important, a new testing environment is needed. Because CPS
is affected by the external environment, it is important to actually reproduce and test the
external environment. CPS's new testing environment requires resolution of the following
constraints: 1. (Diversity) It is difficult to create constraints on various external
environments (natural environment, temperature, humidity, etc.). 2. (Safety) Actual test
environment such as explosion detection, fire detection, accident detection is dangerous.
3. (Efficiency) It takes too much time. In this presentation, we introduce CPS testing
method in a virtual test environment that solves the above three constraints, focusing on
cases.
・ Title: Consideration for SW quality Factor
in the era of industry 4.0 (Focusing on
CPS operational environment)
・ Name: Yong Gyu, Kim
・ Affiliate: Apexsoft Consultant
・ Bio: CMU(Carnegie Mellon University) Software
Engineering Master
Abstract:
- Sharing the factors to be considered and the experience of applying quality verification
method to secure the quality of the SW system in the industrial field adopting the CPS
concept in the era of the fourth industrial revolution centered on artificial intelligence
technology.)
- Explanation of the available reference architectures according to the case studies and
analysis based on experience with CPS application systems or similar systems in areas such
as SCADA and ICS.
- Introduction of risk factors such as potential obstacles and performance degradation when
applying the latest technology, and examples of ways to mitigate it.
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・ Title: The Trend of China's Software
Industry Development
・ Name: Yang Genxing
・ Affiliate: Doctor, Professor, Doctoral Supervisor at
East China University of Science and Technology
・ Bio: Majors: Software Engineering, Intelligence
System, Software Test and Quality, and relative
national standard formation. Expert Member of
Shanghai Information Technology Expert
Committee.
Abstract:
The Trend of China's Software Industry Development
(1) General Situation of Software Industry Development in China and Shanghai in 2018
(2) Key Areas of Software Industry Development in China
(3) Development of Software Engineering and Software Quality Standards
(4) Training of Software Talents with High Skills
(5) Opportunities and Challenges of Software Industry
・ Title: Quantifying Confidence Using
Metrics – an IV&V Perspective
・ Name: Amiruddin Jaafar Sidek
・ Affiliate: Director, Malaysian Software Testing Board
・ Bio: Amir brings more than 30 years of ICT practice
experience in various roles across multiple industry
domains.
Abstract:
Independent Verification and Validation (IV&V) practices involve multiple types of
activities – review, testing, auditing etc – and produce various reports and readings.
- How do we review/analyze these reports and readings leveraging measurements and
metrics?
- What are the tell-tale signs that the product/project is not in the pink of health?
- What does it take to ensure stakeholders have high confidence in software products and
services offered?
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・ Title: Software Process Metrics
・ Name: Halizawati MOHD HALID
・ Affiliate: Head of Quality Assurance Department,
Custommedia Sdn Bhd
・ Bio: Halizawati brings in more than 16 years of work
experience in the Quality Assurance (QA) field with
domain specialty in Information Technology and
Financial.
Abstract:
The software industry uses metrics to quantify the development, operation and maintenance
of software. Software metrics are broadly classified as product metrics, process metrics and
project metrics.
- Product metrics define the product characteristics for example size, design features,
complexity, performance, and quality level.
- Process metrics are metrics pertaining to Process Quality that can be used to improve
software development and maintenance. Software process metrics are associated to the
effort to complete the project, the use of project resources, and the methodology to abide
by. Process metrics are
collected across all projects and over long periods of time. Examples of software process
metrics are cost of quality, testing efficiency and defect removal efficiency.
- Project metrics define the characteristics and execution of the project and are used to
monitor and control the project. Examples include the values of human effort, cost, and
project schedule.
The above metrics are captured to establish and maintain a baseline. This baseline is known
as the Process Capability Baseline (PCB) which represents a quantitative understanding of
the performance of an organization's set of standard processes in support of quality and
process-performance objectives. PCB contains measures which are made available so that
current and future projects are able to use them to plan, predict outcomes and manage effort
and results.
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・ Title: Guidelines of Quality Assurance for
Artificial-Intelligence-based products and
services (QA4AI)
・ Name: Yasuharu Nishi
・ Affiliate: Assistant Professor, The university of
Electro-Communications
・ Bio: The University of Electro-Communications
Assistant Professor 2003/04-Present, SQC Inc.
Director of Consulting Division 2001/04-2003/03
Abstract:
Consortium of QA4AI published the guidelines for quality assurance of machine learning-
based artificial intelligence on 17th
May 2019. In this presentation, he will discuss about
overview and methodology of the guidelines that includes axes of quality evaluation and
technical catalog for ML-bases systems. The guidelines cover some specific ML-based
systems such as generative systems, operational data in process systems, voice user
interface and autonomous driving.
