International Partnership He (Jason) Zhang in Information - - PowerPoint PPT Presentation

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International Partnership He (Jason) Zhang in Information - - PowerPoint PPT Presentation

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Nanjing University International Partnership He (Jason) Zhang in Information Technology 4 June 2018 @ NTNU Nanjing China A cultural city with 2,600 years history the capital of China for ten dynasties, including the Republic of

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Nanjing University

International Partnership in Information Technology

He (Jason) Zhang

4 June 2018 @ NTNU

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Nanjing · China

Beijing Shanghai Hong Kong

❖ A cultural city with 2,600 years history ❖ the capital of China for ten dynasties, including the Republic of

China

❖ A city of excellent specialists for scientific education ❖ 53 general higher universities with over 820,000 college students ❖ 540 various scientific research organisations with 530,000

research staffs

❖ No.1 among all Chinese cities in terms of the portion of

population receiving higher education

❖

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Nanjing University

❖ Data Technologies

❖ Data Mining ❖ mining software process from repository ❖ mining software metrics for process modeling ❖ Natural Language Processing ❖ NLP for grey literature review ❖ NLP in support of qualitative analysis & synthesis

❖ The first Chinese modern university with the combination of

education & research established in 1902

❖ 29 schools, institutes, or departments, 3 of which are the

Department of Computer Science & Technology, Software Institute, and Artificial Intelligence Institute

❖ The top ranked State Key Research Laboratory of Novel

Software Technology in all 32 state key research laboratories in information sciences in past 15 years

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Curricula @ Software Institute

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Nanjing University · DevOps Research Centre

❖ Software Institute ❖ Faculty Members

❖ Prof. He (Jason) Zhang ❖ Assoc. Prof. Dong Shao ❖ Dr. Guoping Rong ❖ Dr. Zheng Li

❖ Students

❖ 8 Ph.D Students ❖ 20+ Master & Thesis Students

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Traditional Research Interests

❖ Software Engineering Process ❖ Empirical Software Engineering ❖ Quality Assurance ❖ Software Architecture ❖ Service-Oriented Computing

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Why DevOps?

❖ “Through 2016, ~80% of outages impacting mission-critical services will be

caused by people and process issues, and more than 50% of those outages will be caused by change / configuration / release integration and hand-

  • ff issues.”


 – Gartner Report

❖ “The Knight Capital Group was an American global financial services firm

[…].[…] Knight was the largest trader in U.S. equities, with a market share

  • f 17.3% on NYSE and 16.9% on NASDAQ. The company agreed to be

acquired by Getco LLC in Dec 2012 after a trading error lost $460 million” 
 – Knight Capital from Wikipedia

❖ This took 45 minutes and was an upgrade error.

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DevOps is about…

❖ “DevOps is a set of practices intended to reduce the time between committing a

change to a system and the change being placed into normal production, while ensuring high quality.”

❖ DevOps is about… ❖ Bringing “agile” methods to operation ❖ Encouraging collaboration between development and operations staff, get

them talking

❖ Shared goals and teams of Devs and Ops ❖

Requirements

  • Treat Operations

personnel as first class stakeholders

  • Get their input

when developing requirements

Development

  • Small teams
  • Limited

coordination

  • Unit tests

Build

  • Build tools
  • Supports

continuous integration

Testing

  • Automated testing
  • Integration testing

Deployment

  • Deployment tools
  • Supports

continuous deployment

Execution

  • Monitoring
  • Responding to error

conditions

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SLIDE 9

DevOps Influences…

❖ DevOps practices will influence industry ❖ the way you organise teams ❖ the way you build systems ❖ even the structure of the systems that you build ❖ DevOps practices will influence research ❖ Software Architecture ❖ split application into small, well-scoped microservices ❖ Software Process ❖ Ops as first-class citizens throughout the lifecycle

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Grand Questions of Interest

❖ What are the impacts of DevOps on software industry

in general, or software practitioners in particular?

❖ How to support software organisations to adopt or

migrate to DevOps?

❖ How to reengineer software technologies and advance

the state-of-the-art of DevOps?

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SLIDE 11

Data Tech. Empirical 
 Methodology

Archit. Quality

Security

Deploy

Process

DevOps

Academic Achievement

Data Mining NLP

Industrial Impact

SLR MLR QRS Doc Engineering Microservices DDD Logging Container DevSecOps API Gateway Secure Arch. Agility CI Lean Kanban CD Automated Process

Toolchain

DL Qualitative Methods Log Analysis

Mobile
 Internet Big Date

Cyber- Physical
 System Smart

autonomous

system

。。。

Application Domains Software Engineering

Fundamental Competency

Cyber- Security

Nanjing University · DevOps Research Centre

DFD

Organisation & Culture

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Fundamental Competency

❖ Empirical Methodologies

❖ Evidence-Based Software Engineering (Systematic Reviews) ❖ methodological issues: data synthesis, quality assessment, validity … ❖ Qualitative Research in Software Engineering ❖ ethnography, field study, grounded theory … ❖ qualitative data analysis and synthesis ❖ Grey Literature in Software Engineering ❖ evidence extraction & assessment of grey literature ❖ multivocal literature review

❖ Data Technologies

❖ Data Mining ❖ mining software process from repository ❖ mining software metrics for process modelling ❖ Natural Language Processing ❖ NLP in support of qualitative analysis & synthesis

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Qualitative Evidence Synthesis

Research Synthesis aggregative

< method >

interpretive

< extracted information > < study design >

boolean logic frequency structured questions

< data >

findings raw data meta-summary content analysis comparative analysis case survey diverse design meta-ethnography comparable design

< purpose >

theory generation understanding grounded theory narrative synthesis thematic synthesis

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SLIDE 14

DevOps Oriented Software Engineering

❖ Toolchain

❖ “DevOps Tooling Technical Report”

❖ Microservices Architecture

❖ Impacts on Quality Attributes ❖ Microservice Granularity ❖ Migration from Monolith to Microservice System ❖ Decomposition for Microservices ❖ constraints ❖ domain-specific design ❖ data-flow diagram ❖ Benchmark Example for Performance Evaluation ❖ pet-store project

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DevOps Tooling

MSBuild MSBuild

Azure Azure

Academic Literature: 144 Academic Literature: 144 : 178 : 178 : 76 : 76 : 117 : 117

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DevOps Tooling

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Data-Flow Driven Service Decomposition

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DevOps Oriented Software Engineering

❖ Quality

❖ Logging Practices

❖ *Security

❖ Secure Architecture ❖ DevSecOps

❖ *Deployment & Operation ❖ Process & Agility

❖ Automated Software Process ❖ Mining Software Process ❖ Software Process Simulation Modelling ❖ hybrid simulation modeling ❖ model verification & validation ❖ Agile / Lean / Kanban Development ❖ Interplay between Process and Product

❖ Organisation & Culture

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Metrics for Process Modelling

Process

… … …

Requirement Design Implementation Review Test Delivery & Maintenance Phase Independent Time(270)

  • HNKL

L DL HGML Project Duration

  • HNKL

L DL HGML Test Duration

  • HNKL

L Fix Duration L Task Duration

  • HNKL

L Review Duration

  • HNKL
  • HNKLD

Work Time

  • HNKL

L Interval between Releases L Lead time L Answer time L Delivery time HGML Domain Discovery Delay HGML Design Filling Delay HGML Analysis Discover Delay HGML New Requirements Feedback Delay L% Requirement Selection Time L Coding Duration

  • HNKL

L DL Rework Duration L Interval between Requirements

  • HNKL

Issue Assessment Duration Requirement Design Implementation Review Test Delivery & Maintenance Phase Independent Defect (246) ML # Injected Requirement Defects

  • Injection Rate

during Design ML # Design Defects

  • Percentage of

Fixed Requirement Defects

  • Design Defect

Density ML ML-HNK MLHGM MLLD MLGCM Injection Rate during Coding ML # Injected Code Defects MLHGM Fix Rate

  • Percentage

Fixed Defects ML # Fixed Defects ML MLLML Detected Defects in Test

  • Defects Detected in

Reviews % ML-HNK Defect Detection Rate

  • Overlap of Detection
  • f Reviewers

ML # Detected Defects

  • Rate of Defect

Delivered ML # Detected Defects after Test ML Released Defect Density CFGLCHGELL Defect Severity ML-HNK MLHGM Defects Injection Rate ML MLGCM Defect Density CFGLCHGELL Defect Type ML # Remaining Defects Requirement Implementation Review Test Delivery & Maintenance Phase Independent Task Size (112) AL

  • Task Size per

Inspection .GLIMCHGL # Inspections GCML Amount of Rework LDL LKMHKCL Amount of Implementation Work MNKLCF Demand for New Features NCKFGML Test Task Size LDL Total Tasks KLHGDLLD

  • HNKLLD

Task Size LHNKLLD Task Required Resource LDLLKMHK Required Tasks NLML # Maintenance Requests CFGLCHGELL Delivery Times Requirement Design Implementation Change (48) GAL # Requirement Changes GALCF Rate of Requirement Changes

  • Change request acceptance Rate
  • Change Request Time Span

GAL # Design Changes GAL # Code Changes Design Implementation Review Test Phase Independent Effort(143) KLHG-HNKL KLHGL KLHGDL KLHGHGML L Total Effort KLHGDL Effort per Item

  • Effort Allocation

Percentage KLHG-HNKL Coding Effort KLHGL Test Effort

  • HNKLGCM

Test Effort Rate KLHGDL Effort per Test Case

  • HNKL

Design Effort

  • Review Effort

KLHGL Rework Effort

  • Meeting Effort
  • Preparation

Effort KLHGDL KLHGLM Fixing Effort Requirement Design Implementation Review Test Delivery & Maintenance Phase Independent Process Factor(106) CFGLCHGELL Time Pressure CFGLCHGELL Coding Method CFGLCHGELL Task Priority CFGLCHGELL Policy CFGLCHGELL Risk Impact CFGLCHGELL Quality Strategy

…

CFGLCHGELL Test Method OCPL # Periodical Reviews CFGLCHGELL Review Adjustment Policy CFGLCHGELL Domain Discovery Factor CFGLCHGELL Analysis Discovery Factor CFGLCHGELL Design Discovery Acceptance Factor CFGLCHGELL New Requirements Feedback Factor CFGLCHGELL New Requirements Scaling Policy CFGLCHGELL Requirements Volatility CFGLCHGELL Release Policy CFGLCHGELL Maintenance Adjustment Factor Design Implementation Review Test Delivery & Phase Independent Development .

  • L

LDL.MKMCHG Development Rate

  • Discover Rate

HNFGML HNFGMLHGM Design Rate

  • Implementatio

n Rate GCMLCF Delivery Rate

  • Review Rate
  • Test Rate
  • HNKLM

LDL Rework Rate CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL KLHGL FL KLHGL KLHGL KLHGL KLHGL HHFL CECMCL#-HNKLD

  • HNKL

LMLL ICCMCHGL-HNK

//

  • KLHGL

CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL AL AL HNEL KLHG$L HGMKCNMCHGL GCML CEL GCML

  • .

L LMLL

  • CAKFL

HGMKHEEHPL CLCHGHNGM

  • HNE

HEOKCEL EHEOKCEL HGMKHEEHPL LLL HKLA KAKIL GMGL /PHKL HKLA KAKIL GMGL /PHKL CFGLCHGELL CFGLCHGELL MNKL CFHGL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL CFGLCHGELL GCM R% GCM R%

  • HNK
  • HNKLM

KLHGL ML ML LDOCL M Risk Start/End Date

…

CFGLCHGELL CFGLCHGELL *HK-HNK

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Legend Process Intenal Process External Resource Intenal Product Intenal Resource External Product Size Detail Measure Product Features Complexity

Maintainability Reliability Quality Time Defect

Task Size

Change Effort Process Factor Development Rate Effectiveness Cost Resource Size Productivity Personnel Continuity Capability Experience

Generates & Detects & Fixes (69) Product External

Overhead

Measures(5) Impacts (15) Impacts(3) Impacts(9) Impacts(17) Extends Extends Extends Extends Extends Extends Extends Extends Extends Measures(9) Changes(6) Releases(9) Extends Extends Extends Extends Extends Extends Extends Extends Extends Measures Measures(1) Measures(1) Measures(1) Measures(5) Measures(7) Impacts(22) Measures(10) Extends Extends Impacts (7) Measures Impacts(3) Requires(1) Impacts(2) Impacts Extends Impacts(4) Impacts (5) Measures(9) Measures(35)

Process Static Attributes Resource Factor

Impacts(11) Measures(12) Mesures(16) Measures (12) Extends Impacts(1) Requires(33) Produces(33) Measures(16) Requires(25) Produces(25) Requires(18) Produces(18) Category

Sub-Category

Label (# evidence) Extends Requires(9) Measures (4) Impacts(7) Impacts(8)

Metrics for Process Modelling

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Industrial Impacts

❖ Synergy by combining research, industry, and education

❖ 《DevOps: Theory, Method, and Practice》(1st DevOps textbook in China) ❖ DevOps curricular and teaching/training platform ❖ Industrial Collaborations on DevOps: Huawei, ZTE, Transwarp, DaoCloud … ❖ 《DevOps · China 2016 Report》 ❖ 《DevOps · China 2018 Report》（available soon） ❖ 《DevOps Tooling Technical Report》 ❖ 《Software Architecture for Practitioners》@ GeekTime ❖ ArchSummit 2017 / 2018 (Shenzhen) ❖ Keynotes & invited talks at CNUTCon 2017 / NASAC 2017 / NJSD 2018 / DOIS 2018 … ❖ DevOps tracks at IAS 2017 & NJSD 2018 ❖ DevOps Special Group of CCF · Software Engineering & System Software Committees