Intelligent Software Engineering: Synergy between AI and Software

Engineering

Tao XieUniversity of Illinois at Urbana-Champaign

taoxie@illinois.edu http://taoxie.cs.illinois.edu/

Artificial Intelligence Software Engineering

Artificial Intelligence

Software Engineering

Intelligent Software Engineering

Intelligent Software Engineering

1st International Workshop on Intelligent Software Engineering (WISE 2017)

Tao XieUniversity of Illinois at

Urbana-Champaign, USA

Abhik RoychoudhuryNational University of Singapore, Singapore

Organizing Committee

Wolfram Schulte Facebook, USA

Qianxiang Wang Huawei, China

Sponsor:

Co-Located with ASE 2017https://isofteng.github.io/wise2017/

Workshop Program8 invited speakers1 panel discussion

https://isofteng.github.io/wise2017/

International Workshop on Intelligent Software Engineering (WISE 2017)

Artificial Intelligence Software Engineering

Artificial Intelligence

Software Engineering

Intelligent Software Engineering

Intelligent Software Engineering

Past: Automated Software Testing

• 10 years of collaboration with Microsoft Research on Pex• .NET Test Generation Tool based on Dynamic Symbolic Execution

• Example Challenges• Path explosion [DSN’09: Fitnex]• Method sequence explosion [OOPSLA’12: Seeker]

• Shipped in Visual Studio 2015/2017 Enterprise Edition• As IntelliTest

• Code Hunt w/ > 6 million (6,114,978) users after 3.5 years• Including registered users playing on www.codehunt.com, anonymous

users and accounts that access api.codehunt.com directly via the documented REST APIs) http://api.codehunt.com/

http://taoxie.cs.illinois.edu/publications/ase14-pexexperiences.pdf

Past: Android App Testing• 2 years of collaboration with Tencent Inc. WeChat testing team

• Guided Random Test Generation Tool improved over Google Monkey

• Resulting tool deployed in daily WeChat testing practice• WeChat = WhatsApp + Facebook + Instagram + PayPal + Uber …

• #monthly active users: 963 millions @2017 2ndQ

• Daily#: dozens of billion messages sent, hundreds of million photos uploaded, hundreds of million payment transactions executed

• First studies on testing industrial Android apps [FSE’16IN][ICSE’17SEIP]

• Beyond open source Android appsfocused by academia

WeChat

http://taoxie.cs.illinois.edu/publications/esecfse17industry-replay.pdfhttp://taoxie.cs.illinois.edu/publications/fse16industry-wechat.pdf

Next: Intelligent Software Testing(?)

• Learning from others working on the same things• Our work on mining API usage method sequences to test the API [ESEC/FSE’09:

MSeqGen] http://taoxie.cs.illinois.edu/publications/esecfse09-mseqgen.pdf

• Visser et al. Green: Reducing, reusing and recycling constraints in program analysis. FSE’12.

• Learning from others working on similar things• Jia et al. Enhancing reuse of constraint solutions to improve symbolic execution..

ISSTA’15.

• Aquino et al. Heuristically Matching Solution Spaces of Arithmetic Formulas to Efficiently Reuse Solutions. ICSE’17.

[Jia et al. ISSTA’15]

Mining and Understanding Software Enclaves (MUSE)

http://materials.dagstuhl.de/files/15/15472/15472.SureshJagannathan1.Slides.pdf

DARPA

Pliny: Mining Big Code to help programmers

(Rice U., UT Austin, Wisconsin, Grammatech)

http://pliny.rice.edu/ http://news.rice.edu/2014/11/05/next-for-darpa-autocomplete-for-programmers-2/

$11 million (4 years)

Program Synthesis: NSF Expeditions in Computing

https://excape.cis.upenn.edu/https://www.sciencedaily.com/releases/2016/08/160815134941.htm

10 millions (5 years)

Software related data are pervasive

Runtime traces

Program logs

System events

Perf counters

…

Usage log

User surveys

Online forum posts

Blog & Twitter

…

Source code

Bug history

Check-in history

Test cases

Keystrokes

…

Software Analytics

Vertical

Horizontal

Information Visualization

Data Analysis Algorithms

Large-scale Computing

Software analytics is to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks around software and services.

In Collaboration with Microsoft Research Asia

http://taoxie.cs.illinois.edu/publications/malets11-analytics.pdf

Past: Software Analytics

• StackMine [ICSE’12]: performance debugging in the large• Data Source: Performance call stack traces from Windows end users

• Analytics Output: Ranked clusters of call stack traces based on shared patterns

• Impact: Deployed/used in daily practice of Windows Performance Analysis team

• XIAO [ACSAC’12]: code-clone detection and search• Data Source: Source code repos (+ given code segment optionally)

• Analytics Output: Code clones

• Impact: Shipped in Visual Studio 2012; deployed/used in daily practice of Microsoft Security Response Center

In Collaboration with Microsoft Research Asia

Internet

Past: Software Analytics• Service Analysis Studio [ASE’13]: service incident management

• Data Source: Transaction logs, system metrics, past incident reports

• Analytics Output: Healing suggestions/likely root causes of the given incident

• Impact: Deployed and used by an important Microsoft service (hundreds of millions of users) for incident management

In Collaboration with Microsoft Research Asia

Next: Intelligent Software Analytics(?)

Microsoft Research Asia - Software Analytics Group - Smart Data DiscoveryIN4: INteractive, Intuitive, Instant, INsights

Quick Insights -> Microsoft Power BI

Gartner Magic Quadrant for Business

Intelligence & Analytics Platforms

Microsoft Research Asia - Software Analytics Group

https://www.hksilicon.com/articles/1213020

Open Topics in Intelligent Software Engineering (ISE)

• How to determine whether a software engineering tool is indeed “intelligent”?• Turing test for such tool?

• What sub-areas/problems in ISE shall the research community invest efforts on as high priority?

• How to turn ISE research results into industrial/open source practice?

• …

Artificial Intelligence Software Engineering

Artificial Intelligence

Software Engineering

Intelligent Software Engineering

Intelligent Software Engineering

White-House-Sponsored Workshop (2016 June 28)

http://www.cmu.edu/safartint/

Self-Driving Tesla Involved in Fatal Crash (2016 June 30)

http://www.nytimes.com/2016/07/01/business/self-driving-tesla-fatal-crash-investigation.html

“A Tesla car in autopilot crashed into a trailer because the autopilot system failed to recognize the trailer as an obstacle due to its “white color against a brightly lit sky” and the “high ride height”

http://www.cs.columbia.edu/~suman/docs/deepxplore.pdf

Microsoft's Teen Chatbot TayTurned into Genocidal Racist (2016 March 23/24)

http://www.businessinsider.com/ai-expert-explains-why-microsofts-tay-chatbot-is-so-racist-2016-3

"There are a number of precautionary steps they [Microsft] could have taken. It wouldn't have been too hard to create a blacklist of terms; or narrow the scope of replies. They could also have simply manually moderated Tay for the first few days, even if that had meant slower responses."

“businesses and other AI developers will need to give more thought to the protocols they design for testing and training AIs like Tay.”

• An example• Declaration from the PuTTY Website for Google’s search result

change

• This declaration suggests that Google’s search results for “putty” at some time may not be satisfactory and may cause confusions of the users.

Motivation: Testing Search Engines

23

Our Past Work on Testing Search Engines

• Given a large set of search results, find the failing tests from them• Test oracles: relevance judgments

• Test selection• Inspect parts of search results for some queries by mining search results of all

queries

24

Queries and

Search ResultsFeature Vectors

(Itemsets)Association

Rules

Detecting

Violations

http://taoxie.cs.illinois.edu/publications/ase11-searchenginetest.pdf

[ASE 2011]

• Query items• Query words, query types, query length, etc.

• Search result items• Domain, domain’s Alexa rank, etc.

• Query-result matching items• Whether the domain name has the query, whether the title has the query, etc.

• Search engine items• Search engine names

Mining Output Rules

25

• SE:bing, Q:boston colleges, QW:boston, QW:colleges, TwoWords, CommonQ, top10:searchboston.com, top1:searchboston.com, top10:en.wikipedia.org, …, SOMEGE100K, SOMELE1K

• SE:bing, Q:day after day, QW:day, QW:after, ManyWords, CommonQ, top10:en.wikipedia.org, top1:en.wikipedia.org, top10:dayafterday.org, …, SOMEGE100K, SOMELE1K

Example Itemsets

26

Mining Association Rules

• Mining frequent itemsets with length constraint • An itemset is frequent if its support is larger than the min_support

{SE:bing, top10:en.wikipedia.org}

• Generating rules with only one item in the right hand side• For each item xi in Y, generate a rule Y-xi => xi

SE:bing=>top10:en.wikipedia.org

27

Experiments

• Search results• Use the Web services of Google and Bing to collect the top 10

search results of each query from December 25, 2010 to April 21, 2011

• 390797 ranked lists of search results (each list contains the top 10 search results)

28

The Mined Rules

• Mining from one search engine' results in one day• Google's search results on Dec. 25, 2010

• minsup = 20, minconf = 0.95, and maxL = 3

29

The Mined Rules

• Mining from multiple search engines' results in one day• Google and Bing's search results on Dec. 25, 2010

• minsup = 20, minconf = 0.95, and maxL = 3

• Rules 9-12 show the different opinions of search engines to certain Websites

30

The Mined Rules

• Mining from one search engine' results in multiple days• Google's search results from December 25, 2010 to March 31, 2011.

• minsup = 20, minconf = 0.95, and maxL = 2

• Rules 13-18 show the rules about the top 1 results for the queries

31

Example Violations

• Search results of Bing on April 1st, 2011 violate the following rule

• The actual result of Bing

http://www.jcu.edu/index.php

points to the homepage of the John Carroll University, not easy to get the answer of the query

32

Our Recent Work: Multiple-Implementation Testing of Machine Learning Software

Faults Detected for kNN and Naive Bayes Implementations

Detected Fault Types

Other’s Work at Columbia/Lehigh U.:SOSP 2017 Best Paper Award

http://www.cs.columbia.edu/~suman/docs/deepxplore.pdf

Our Most Recent Work:Malware Detection in Adversarial Settings:

Exploiting Feature Evolutions and Confusions in Android Apps

Wei Yang, Deguang Kong, Tao Xie and Carl A. Gunter

Annual Computer Security Applications Conference (ACSAC 2017)

http://taoxie.cs.illinois.edu/publications/acsac17-malware.pdf

http://web.engr.illinois.edu/~weiyang3/

Wei Yang on the job market!

Machine learning (ML) in adversarial settings

38

• The development of machine learning is algorithm-oriented, often neglecting real-world constraints: machine budget, time cost, security, privacy etc. • The real world is complex, uncertain and adversarial.

• Our work• Robustness of ML algorithms in security systems (well-known).

• Practicability of previous proposed attacks on ML-based security system (something new).• We use mobile malware detection systems as an example for study.

• Complexity (handled by logic), uncertainty (handled by probability) and, adversarial (focus of our work).

Robustness of ML algorithms in security systems

39

A Dataset

Training Data

TestData

Training

Feature Extraction

Feature Vector

ML Algorithm

Trained Classifier

Result

• Conventionally, to train an ML model, researchers separate training data and test data from the same dataset.

• Overly-strong assumption: training data shares similar stationary distribution as test data (i.e., training data is representative).

Robustness of machine learning algorithms in security systems

40

A Dataset

Training Data

TestData

Training

Feature Extraction

Feature Vector

ML Algorithm

Trained Classifier

Result

• In adversarial settings, such assumption often does not hold• Training data and test data may not come from the same data set.

• Poisoning attack: engineering on features or labels of training data • Compromise the discriminant classifier.

• Evade attack: engineering on features of testing data• Change discriminant feature values to indiscriminant ones.

Focus: Evasion Attack on Mobile Malware Detection• Goals: Understand classifier robustness;

Build better classifiers (or give up)

• Existing work:

43

Existing work

• Purely based on mathematics.• Making slight perturbations (e.g., 𝛿𝑥2) to incur dramatic change on output (e.g., 𝑂𝑢𝑡𝑝𝑢𝑡(𝑥1, 𝑥2)).

• Example: • Derivative-based approach

44

Malicious

Benign

https://arxiv.org/abs/1511.07528

What is missing: Practicability

• These proposed attacks manipulate the feature vectors of a malware in theory w/o considering the feasibility/impact of the mutations on the malware’s code. • Mutations may cause compilation errors.

• Mutations may crash the app, cause undesirable behaviors, or disable malicious functionalities.

45

Three practical constraints to craft a realistic attack

• Preserving Malicious Behaviors• Maintain the original malicious purposes.

• Maintaining the Robustness of Apps• Robust enough to be installed and executed in mobile devices.

• Evading Malware Detectors w/ little or no knowledge of the model• Identify and change feature values to evade detection.

46

Malware Recomposition Variation (MRV)

• Malware Evolution Attack (existing malware evolution histories )• Reapply feature mutations in malware evolution to create new malware

variants.

• Malware Confusion Attack (existing evasive malware)• Mutate a malware’s feature values to mimic classified benign apps’ to create

new malware variants.

• Insight: feature mutations are less likely to break the apps when the mutations follow feature patterns of existing malware.

47

Intuition: Evolving space of malware

• Malware Evolution: make a feature vector Vi that is correctly classified as malware to be misclassified as benign apps by mutating Vi’s feature values.

Three options:• Mutating Vi to be exactly same as a benign feature

vector (e.g., B1)• Mutating Vi to be exactly same as a malware feature

vector that is misclassified as benign (e.g., M2)• Mutating Vi to a new feature vector Vn that can be

potentially classified as benign (e.g., Mv)

Malware Detection

• A feature vector space V, • The feature vectors of existing

benign apps B,• The feature vectors of existing

malware M,• The feature vectors detected by

detection model D, • The feature vectors of all

potential malware M’.𝑉

𝐵 𝑀𝐷

𝑀′

Malware Detection

If the feature selection is perfect (i.e., all differences between benign apps and malware are captured by selected features),no malware and benign apps should be projected to the same feature vector.

𝑉

𝐵 𝑀𝐷

𝑀′

Insight 1: Differentiability of Feature Set

Malware detection usually performs poorly in differentiating the malware and benign apps with the same feature vector.

We try to convert malware withunique malicious features (i.e., M n (B \M)) to possess thefeature vectors shared by benign apps (i.e., B\M)).

𝑉

𝐵 𝑀𝐷

𝑀′

Attack 1: Malware confusion attack

Mutate a malware’s feature values existing in only malware to the values existing in both malware and benign apps

E.g., M1 -> M2

Insight 2: Robustness of detection model

• Detection models are limited to detect existing malware.

• A robust malware detection model should aim to detect malware variants produced through known transformation.

The robustness of a detection model can be represented by

𝑉

𝐵 𝑀𝐷

𝑀′

Attack 2: Malware evolution attack

• Mutate malware feature values to the “blind spots” of malware detection.

• Mimic feature mutations in the actual evolution of malware in the real world.

• We conduct a phylogenetic analysis for each family of malware

• E.g., Apply the mutation

M1->M2 (e.g., c->c’ in 𝐹3)

on M3 to derive 𝑀𝑣

Why MRV works

• Large feature set has numerous non-essential features • non-informative or even misleading features.

• Observation 1: Malware detectors often confuse non-essential features in code clones as discriminative features.• Learning-based detectors may regard non-essential features (e.g., minor implementation detail) in

code clones as major discriminant factor

• Observation 2: Using a universal set of features for all malware families would result in a large number of non-essential features for each family opportunities to be mutated to evade detection while preserving malicious purposes• Features essential to malicious behaviors are different for each malware family

55

Overview

56

Feature Model

• A substitute model• Summarize essential features (i.e., security-sensitive resources) and

contextual features (e.g., when, where, how the security-sensitive resources are obtained and used) commonly used in malware detection

• Transferability property• Some adversarial samples generated based on the substitute model can also

evade the original detectors

57

Approach

• Mutation strategy synthesis• Phylogenetic analysis for evolution attack

• Similarity metric for confusion attack

• Program mutation• Program transplantation/ refactoring

58

Practicability of attacks

• Checking the preserving of malicious behaviors• Our impact analysis is based on the insight that the component-based nature of

Android constrains the impact of mutations within certain components.

• Checking the robustness of mutated apps• Each mutated app is tested against 5,000 events randomly generated by Monkey

to ensure that the app does not crash.

59

Results

60

Strengthening the robustness of detection

• Adversarial Training• Randomly choose half of our generated malware variants into the training set

to train the model

• Variant Detector• Create a new classifier called variant detector to detect whether an app is a

variants derived from existing malware.

• Weight Bounding• Constrain the weight on a few dominant features to make feature weights

more evenly distributed.

61

Results

62

Artificial Intelligence Software Engineering

Artificial Intelligence

Software Engineering

Intelligent Software Engineering

Intelligent Software Engineering