synergy of human and artificial intelligence in software engineering

The Synergy of Human and Artificial Intelligence in Software Engineering

Tao Xie

North Carolina State UniversityRaleigh, NC, USA

RAISE 2013

Turing Test Tell Machine and Human Apart

Human vs. Machine Machine Better Than Human?

IBM's Deep Blue defeated chess champion Garry Kasparov in 1997

IBM Watson defeated top human Jeopardy! players in 2011

Global Trend: Artificial Intelligence Replacing Human Intelligence

Google’s driverless car

Microsoft's instant voice translation tool

IBM Watson as Jeopardy! player

CAPTCHA: Human Intelligence is Better

"Completely Automated Public Turing test to tell Computers and Humans Apart"

Human-Computer Interaction

Movie: Minority Report

CNN News

iPad

Human-Centric Software Engineering

…

Task Allocation of Artificial and Human IntelligenceMachine is better at task set A

Mechanical, tedious, repetitive tasks, … Ex. solving constraints along a long path

Human is better at task set B Intelligence, human intent, abstraction,

domain knowledge, … Ex. local reasoning after a loop, recognizing

naming semantics

= A U

B8

Mutually Enhanced Demands on Artificial and Human Intelligence

Malaysia Airlines Flight 124 @2005Lisanne Bainbridge, "Ironies of Automation”, Automatica 1983 .

Ironies of Automation“Even highly automated systems, such as electric power networks, need human beings... one can draw the paradoxical conclusion that automated systems still are man-machine systems, for which both technical and human factors are important.”

“As the plane passed 39 000 feet, the stall and overspeed warning indicators came on simultaneously—something that’s supposed to be impossible, and a situation the crew is not trained to handle.” IEEE Spectrum 2009

Mutually Enhanced Demands on Artificial and Human Intelligence

Malaysia Airlines Flight 124 @2005Lisanne Bainbridge, "Ironies of Automation”, Automatica 1983 .

Ironies of Automation“The increased interest in human factors among engineers reflects the irony that the more advanced a control system is, so the more crucial may be the contribution of the human operator.”

Takeaway Messages

Don’t forget human intelligence Using your tools as end-to-end solutions Helping your tools

Don’t forget cooperations of human and tool intelligence; human and human intelligence Human can help your tools too Human and human could work together to help

your tools, e.g., crowdsourcing

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Takeaway Messages




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Google Scholar: “Pointer Analysis”

“Pointer Analysis: Haven’t We Solved This Problem Yet?” [Hind PASTE 2001]

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“During the past 21 years, over 75 papers and 9 Ph.D. theses have been published on pointer analysis. Given the tones of work on this topic one may wonder, “Haven't we solved this problem yet?'' With input from many researchers in the field, this paper describes issues related to pointer analysis and remaining open problems.”Michael Hind. Pointer analysis: haven't we solved this

problem yet?. In Proc. ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and Engineering (PASTE 2001)

“Pointer Analysis: Haven’t We Solved This Problem Yet?” [Hind PASTE 2001]

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Section 4.3 Designing an Analysis for a Client’s Needs

“Barbara Ryder expands on this topic: “… We can all write an unbounded number of papers that compare different pointer analysis approximations in the abstract. However, this does not accomplish the key goal, which is to design and engineer pointer analyses that are useful for solving real software problems for realistic programs.”

Google Scholar: “Clone Detection”

Some Success Stories of Applying Clone Detection

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Zhenmin Li, Shan Lu, Suvda Myagmar, and Yuanyuan Zhou. CP-Miner: a tool for finding copy-paste and related bugs in operating system code. In Proc. OSDI 2004.

MSRAXIAO

Yingnong Dang, Dongmei Zhang, Song Ge, Chengyun Chu, Yingjun Qiu, and Tao Xie. XIAO: Tuning Code Clones at Hands of Engineers in Practice. In Proc. ACSAC 2012,

MSR 2011 Keynote by YY Zhou: Connecting Technology with Real-world Problems – From Copy-paste Detection to Detecting Known Bugs

Human Intelligence to Determine What are Serious Bugs

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XIAO: Clone Detection@MSRA

Available in Visual Studio 2012Searching similar snippets

for fixing bug once

Finding refactoring opportunity

Yingnong Dang, Dongmei Zhang, Song Ge, Yingjun Qiu, and Tao Xie. XIAO: Tuning Code Clones at Hands of Engineers in Practice. In Proc. Annual Computer Security Applications Conference (ACSAC 2012)

XIAO Code Clone Search service integrated into workflow of Microsoft Security Response Center (MSRC)

Microsoft Technet Blog about XIAO:We wanted to be sure to address the vulnerable code wherever it appeared across the Microsoft code base. To that end, we have been working with Microsoft Research to develop a “Cloned Code Detection” system that we can run for every MSRC case to find any instance of the vulnerable code in any shipping product. This system is the one that found several of the copies of CVE-2011-3402 that we are now addressing with MS12-034.

http://blogs.technet.com/b/srd/archive/2012/05/08/ms12-034-duqu-ten-cve-s-and-removing-keyboard-layout-file-attack-surface.aspx



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XIAO: Enabling Human Intelligence

XIAO enables code clone analysis withHigh scalability, High compatibilityHigh tunability: what you tune is what you getHigh explorability:

1. Clone navigation based on source tree hierarchy2. Pivoting of folder level statistics3. Folder level statistics4. Clone function list in selected folder5. Clone function filters6. Sorting by bug or refactoring potential7. Tagging

1 2 3 4 5 6

7

1. Block correspondence2. Block types3. Block navigation4. Copying5. Bug filing6. Tagging

1

2

3

4

1

6

5

How to navigate through the large number of detected clones? How to quickly review a pair of clones?

"Are Automated Debugging [Research] Techniques Actually Helping Programmers?"

50 years of automated debugging research N papers only 5 evaluated with actual

programmers“

”Chris Parnin and Alessandro Orso. Are automated debugging techniques actually helping programmers?. In Proc. ISSTA 2011

Human Factors in Real World Academia

Tend to leave human out of loop (involving human makes evaluations difficult to conduct or write)

Tend not to spend effort on improving tool usability ▪ tool usability would be valued more in HCI than in SE▪ too much to include both the approach/tool itself and

usability/its evaluation in a single paper

Real-world Often has human in the loop (familiar IDE integration,

social effect, lack of expertise/willingness to write specs,…)

Examples Agitar [ISSTA 2006] vs. Daikon [TSE 2001] Test generation in Pex based on constraint solving

NSF Workshop on Formal methods

Goal: to identify the future directions in research in formal methods and its transition to industrial practice.

The workshop will bring together researchers and identify primary challenges in the field, both foundational, infrastructural, and in transitioning ideas from research labs to developer tools.

http://goto.ucsd.edu/~rjhala/NSFWorkshop/

Example Barriers Related to Human Factors “Lack of education amongst

practitioners” “Education of students in logic and

design for verification” “Expertise required to create and use a

verification tool. E.g., both Astre for Airbus and SDV for Windows drivers were closely shepherded by verification experts.”

“Tools require lots of up-front effort (e.g., to write specifications)”

“User effort required to guide verification tools, such as assertions or specifications”

Example Barriers Related to Human Factors “Not integrated with standard

development flows (testing)” “Too many false positives and no ranking

of errors” “General usability of tools, in terms of

false alarms and error messages. The Coverity CACM paper pointed out that they had developed features that they do not deploy because they baffle users. Many tools choose unsoundness over soundness to avoid false alarms.”

Example Barriers Related to Human Factors “The necessity of detailed specifications

and complex interaction with tools, which is very costly and discouraging for industrial, who lack high-level specialists.”

“Feedback to users. It’s difficult to explain to users why automated verification tools are failing. Counterexamples to properties can be very difficult for users to understand, especially when they are abstract, or based on incomplete environment models or constraints.”

Automation in Software Testing

2010 Dagstuhl Seminar 10111

Practical Software Testing: Tool Automation and Human Factors

http://www.dagstuhl.de/programm/kalender/semhp/?semnr=1011



Automation in Software Testing



Human Factors




Human-Centric SE Example: Whyline

Andy Ko and Brad Myers. Debugging Reinvented: Asking and Answering Why and Why Not Questions about Program Behavior. In Proc. ICSE 2008

Takeaway Messages




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Reflexion Models Motivation

Architecture recovery is challenging (abstraction gap)

Human typically has high-level view in mind Repeat

Human: define/update high-level model of interest Tool: extract a source model Human: define/update declarative mapping

between high-level model and source model Tool: compute a software reflexion model Human: interpret the software reflexion modelUntil happy

Gail C. Murphy, David Notkin. Reengineering with Reflection Models: A Case Study. IEEE Computer 1997

State-of-the-Art/Practice Test Generation Tools

Running Symbolic PathFinder ...…=============================

========================= results

no errors detected=============================

========================= statistics

elapsed time: 0:00:02states: new=4, visited=0,

backtracked=4, end=2search: maxDepth=3, constraints=0choice generators: thread=1, data=2heap: gc=3, new=271, free=22instructions: 2875max memory: 81MBloaded code: classes=71, methods=884

…

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Challenges Faced by Test Generation Tools

object-creation problems (OCP) - 65% external-method call problems (EMCP) – 27%

Total block coverage achieved is 50%, lowest coverage 16%.

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Ex: Dynamic Symbolic Execution (DSE) /Concolic Testing [Godefroid et al. 05][Sen et al. 05][Tillmann et al. 08]

Instrument code to explore feasible paths Challenge: path explosion

When desirable receiver or argument

objects are not generated

Example Object-Creation Problem

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A graph example from QuickGraph library

Includes two classes GraphDFSAlgorithm

GraphAddVertexAddEdge: requires

both vertices to be in graph

00: class Graph { …03: public void AddVertex (Vertex v) {04: vertices.Add(v); // B1 }06: public Edge AddEdge (Vertex v1, Vertex v2) {07: if (!vertices.Contains(v1))08: throw new VNotFoundException(""); 09: // B210: if (!vertices.Contains(v2))11: throw new VNotFoundException("");12: // B314: Edge e = new Edge(v1, v2);15: edges.Add(e); } }

//DFS:DepthFirstSearch18: class DFSAlgorithm { … 23: public void Compute (Vertex s) { ...24: if (graph.GetEdges().Size() > 0) { // B425: isComputed = true;26: foreach (Edge e in graph.GetEdges()) {27: ... // B528: }29: } } } 33

[OOPSLA 11]

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Test target: Cover true branch (B4) of Line 24

Desired object state: graph should include at least one edge

Target sequence:

Graph ag = new Graph();Vertex v1 = new Vertex(0);Vertex v2 = new Vertex(1);ag.AddVertex(v1);ag.AddVertex(v2);ag.AddEdge(v1, v2);DFSAlgorithm algo = new

DFSAlgorithm(ag);algo.Compute(v1);

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//DFS:DepthFirstSearch18: class DFSAlgorithm { … 23: public void Compute (Vertex s) { ...24: if (graph.GetEdges().Size() > 0) { // B425: isComputed = true;26: foreach (Edge e in graph.GetEdges()) {27: ... // B528: }29: } } }

Example Object-Creation Problem

[OOPSLA 11]


object-creation problems (OCP) - 65% external-method call problems (EMCP) – 27%


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Typically DSE instruments or explores only methods @ project under test;Third-party API external methods (network, I/O, ..):

• too many paths• uninstrumentable

Example External-Method Call Problems (EMCP)

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Xusheng Xiao, Tao Xie, Nikolai Tillmann, and Jonathan de Halleux. Precise Identification of Problems for Structural Test Generation. In Proc. ICSE 2011

What to Do Next?



Conventional Wisdom: Improve Automation Capability

Tackling object-creation problems Seeker [OOSPLA 11] , MSeqGen [ESEC/FSE 09]

Covana [ICSE 2011], OCAT [ISSTA 10]Evacon [ASE 08], Symclat [ASE 06]

Still not good enough (at least for now)! ▪ Seeker (52%) > Pex/DSE (41%) > Randoop/random

(26%)

Tackling external-method call problems DBApp Testing [ESEC/FSE 11], [ASE 11]

CloudApp Testing [IEEE Soft 12]

Deal with only common environment APIs

@NCSU ASE

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Test target: Cover true branch (B4) of Line 24

Desired object state: graph should include at least one edge

Target sequence:

Graph ag = new Graph();Vertex v1 = new Vertex(0);Vertex v2 = new Vertex(1);ag.AddVertex(v1);ag.AddVertex(v2);ag.AddEdge(v1, v2);DFSAlgorithm algo = new

DFSAlgorithm(ag);algo.Compute(v1);

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//DFS:DepthFirstSearch18: class DFSAlgorithm { … 23: public void Compute (Vertex s) { ...24: if (graph.GetEdges().Size() > 0) { // B425: isComputed = true;26: foreach (Edge e in graph.GetEdges()) {27: ... // B528: }29: } } }

Example Object Creation Problem (OCP)

Unconventional Wisdom: Human Can Help! Object Creation Problems (OCP)Tackle object-creation problems with Factory Methods

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Unconventional Wisdom: Human Can Help! External-Method Call Problems (EMCP)Tackle external-method call problems with Mock Methods or Method InstrumentationMocking System.IO.File.ReadAllText

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Cooperation Between Human and Machine

Human-Assisted Computing Driver: tool Helper: human Ex. Covana [Xiao et al. ICSE 2011]

Human-Centric Computing Driver: human Helper: tool Ex. Coding duels @Pex for Fun

Interfaces are important. Contents are important too!

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Human-Assisted ComputingMotivation

Tools are often not powerful enough Human is good at some aspects that tools are not

What difficulties does the tool face? How to communicate info to the user to get help?

How does the user help the tool based on the info?

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Iterations to form Feedback Loop

Cooperation Between Human and Machine

Human-Assisted Computing Driver: tool Helper: human Ex. Covana [Xiao et al. ICSE 2011]

Human-Centric Computing Driver: human Helper: tool Ex. Coding duels @Pex for Fun

Interfaces are important. Contents are important too!

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Microsoft Research Pex for FunTeaching/Learning CS via Interactive Gaming

1,230,309 clicked 'Ask Pex!'

www.pexforfun.com

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Nikolai Tillmann, Jonathan De Halleux, Tao Xie, Sumit Gulwani and Judith Bishop. Teaching and Learning Programming and Software Engineering via Interactive Gaming. In Proc. ICSE 2013 SEE.

Behind the Scene of Pex for Fun

Secret Implementation class Secret {

public static int Puzzle(int x) { if (x <= 0) return 1; return x * Puzzle(x-1); }}

Player Implementation

class Player { public static int Puzzle(int x) { return x; }}

class Test {public static void Driver(int x) { if (Secret.Puzzle(x) != Player.Puzzle(x)) throw new Exception(“Mismatch”); }}

behaviorSecret Impl == Player Impl

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Human-Centric Computing

Coding duels at http://www.pexforfun.com/ Brain exercising/learning while having fun Fun: iterative, adaptive/personalized, w/ win

criterion Abstraction/generalization, debugging,

problem solving

Brain exercising

http://www.pexforfun.com/

Coding Duel Competition @ICSE 2011

Coding Duels for Course Assignments

@Grad Software Engineering Course

http://pexforfun.com/gradsofteng

Observed Benefits• Automatic Grading• Real-time Feedback (for Both Students and Teachers)• Fun Learning Experiences



Example User Feedback

“It really got me *excited*. The part that got me most is about spreading interest in teaching CS: I do think that it’s REALLY great for teaching | learning!”

“I used to love the first person shooters and the satisfaction of blowing away a whole team of Noobies playing Rainbow Six, but this is far more fun.”

“I’m afraid I’ll have to constrain myself to spend just an hour or so a day on this really exciting stuff, as I’m really stuffed with work.”

X

Human-Human Cooperation: Pex for Fun (Crowdsourcing)

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Internet

class Secret { public static int Puzzle(int x) { if (x <= 0) return 1; return x * Puzzle(x-1); } }

Everyone can contribute Coding duels Duel solutions

Human-Human Cooperation: Puzzle Games (Crowdsourcing)

InternetPuzzle Games Made from Difficult Constraints or Object-Creation Problems

Supported by MSR SEIF Award

Ning Chen and Sunghun Kim. Puzzle-based Automatic Testing: bringing humans into the loop by solving puzzles. In Proc. ASE 2012

http://www.cs.washington.edu/verigames/

Human-Human/Tool Cooperation: Performance Debugging in the Large

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Pattern Matching

Bug update

Problematic Pattern

Repository

Bug Database

Trace analysis

Bug filing

StackMine [Han et al. ICSE 12]

Trace StorageTrace collection

Internet

Shi Han, Yingnong Dang, Song Ge, Dongmei Zhang, and Tao Xie. Performance Debugging in the Large via Mining Millions of Stack Traces. In Proc. ICSE 2012

StackMine: Industry Impact

“We believe that the MSRA tool is highly valuable and much more efficient for mass trace (100+ traces) analysis. For 1000 traces, we believe the tool saves us 4-6 weeks of time to create new signatures, which is quite a significant productivity boost.”

- from Development Manager in WindowsHighly effective new issue

discovery onWindows mini-hang

Continuous impact on future Windows versions

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Shi Han, Yingnong Dang, Song Ge, Dongmei Zhang, and Tao Xie. Performance Debugging in the Large via Mining Millions of Stack Traces. In Proc. ICSE 2012

Takeaway Messages




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Summary: Cooperative Testing and Analysis

Human-Assisted Computing

Human-Centric Computing

Human-Human Cooperation

Acknowledgment Wonderful current/former students@NCSU ASE

Collaborators, especially those from Microsoft Research Redmond/Asia, Peking University

Colleagues who gave feedback and inspired me

NSF grants CCF-0845272, CCF-0915400, CNS-0958235, ARO grant W911NF-08-1-0443, an NSA Science of Security, Lablet grant, a NIST grant, a 2011 Microsoft Research SEIF Award

Thank you!

Questions ?

https://sites.google.com/site/asergrp/

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