lightweight automated testing with adaptation-based programming alex groce, alan fern, jervis pinto,...

Lightweight Automated Testing with Adaptation-Based Programming

Alex Groce, Alan Fern, Jervis Pinto, Tim Bauer, Mohammad Amin Alipour, Martin Erwig and Camden

Lopez

Oregon State University

Part I: Lightweight Testing

API-Based Testing

• Stateful software system• Various actions (e.g. method calls) that cause

state to change• Some properties to check (e.g., at minimum,

doesn’t crash)• Typical examples: container classes, file

systems, databases, spacecraft command modules…

Need for Lightweight Methods

• What is a lightweight automated testing method?

– 1. Easy enough to implement that it is essentially available for all languages and environments: if it doesn’t exist, anyone can code it up in an afternoon



– 2. Easy enough to use that any programmer interested in writing automated tests can quickly code up a harness for small, moderate complexity, modules



– 3. Fast enough to produce results quickly, so automated testing can be pursued if useful and abandoned if not productive• And debugged if useful but not done right yet!


• What is not a lightweight automated testing method?

– Model checking and concolic testing typically fail all three tests, to date

– In particular, they fail the first test (easy to implement) badly, and fail the second test (easy to use) much of the time

Typical Lightweight Automated Testing

• The archetypal lightweight automated testing method is random testing

Random Test Harnesspublic enum TestOp implements java.io.Serializable {

INSERT,REMOVE,FIND;public static final Set<TestOp> AllVals = unmodifiableSet(EnumSet.allOf(TestOp.class));

}

for (int i = 0; i < NUM_ITERATIONS; i++) {SUT = new SplayTree(); // Create an empty container at beginning of each test caseOracle = new BinarySearchTree(); // Empty oracle containerfor (int j = 0; j < M; j++) {

TestOp o = randomElement (TestOp.AllVals)TestVal v = randomElement (TestVals.AllVals);switch (o) {

case INSERT:r1 = SUT.insert(v); r2 = Oracle.insert(v); break;

case REMOVE:r1 = SUT.remove(v); r2 = Oracle.remove(v); break;

case FIND:r1 = SUT.find(v); r2 = Oracle.find(v); break;

}assert ((r1 == null && r2 == null) || r1.equals(r2)); // Behavior should

match}

}

Test Engineer

Problems with Random Testing?

• Works badly for, e. g. heap structures [Visser et al. ISSTA 2006, Sharma et al. FASE 2011]

• Feedback can help, but if specialized to system, requires tons of engineer effort [Groce et al. ICSE 2007]

• What if we used machine learning to learn feedback for each software system?– Reinforcement learning: system takes an action

(chooses inputs), receives reward based on how well that choice performed; iterates and refines policy for making choices

Part II: Testing with Adaptation-Based Programming

Like Random Testing, but Different

• Idea: replace calls to pseudorandom number generator with calls to library for reinforcement learning

• Reward good tests to influence future policy choices: will start behaving like random testing, eventually do a kind of feedback

• What rewards? Finding faults is too rare– Reward actions that improved total test coverage

ABP Test Harness

Test Engineer

public enum TestOp implements java.io.Serializable {INSERT,REMOVE,FIND;public static final Set<TestOp> AllVals = unmodifiableSet(EnumSet.allOf(TestOp.class));

}AdaptiveProcess test = AdaptiveProcess.init();HashSet<String> states = new HashSet<String>(); // Store all states visitedAdaptive<String,TestOp>opChoice = test.initAdaptive(String.class,TestOp.class);Adaptive<String,TestVal>valChoice = test.initAdaptive(String.class,TestVal.class);for (int i = 0; i < NUM_ITERATIONS; i++) {

SUT = new SplayTree(); // Create an empty container at beginning of each test caseOracle = new BinarySearchTree(); // Empty oracle containerString context = SUT.toString(); // The state is simply a linearization of the SplayTreefor (int j = 0; j < M; j++) {

TestOp o = opChoice.suggest(context, TestOp.AllVals); // Used just like pseudo-random number generator

TestVal v = valChoice.suggest(context, TestVal.AllVals).ordinal();switch (o) {

case INSERT:r1 = SUT.insert(v); r2 = Oracle.insert(v); break;

case REMOVE:r1 = SUT.remove(v); r2 = Oracle.remove(v); break;

case FIND:r1 = SUT.find(v); r2 = Oracle.find(v); break;

}assert ((r1 == null && r2 == null) || r1.equals(r2)); // Behavior should matchcontext = SUT.toString(); // Update the contextif (!states.contains(context)) { // Is this a new state?

states.add(context);test.reward(1000); // Good work, AdaptiveProcess test, you found a new state!

}}test.endEpisode();

}

How Does it Perform?

Problems with Reinforcement Testing?

• We’re coupon collectors, not planners

• Want to hit many different coverage targets– Not hit any particular target with minimum cost

• RL assumes a stationary reward:– Hitting a coverage target “should be” worth as

much the fifth time as the first time

Need to Adapt RL Algorithms

• Use of off-the-shelf RL works as a good lightweight alternative to random testing

• But maybe can do better with an algorithm tailored to the nature of software exploration

• Need to adapt/create ML algorithms– Not just use off-the-shelf tools– We need more collaborations between verification

experts and ML experts!– Paper has some ideas on where to go (e.g. MCTS)

Thank you!Questions?

How to Test?

• Sequence of calls & checks

• Generated how?– Model checking– Concolic testing

• Also: is there a good model checker out there for Python? Concolic testing for Ruby? Does anyone have a good motivation to write one? How long would it take?

• Model checking and concolic testing are too heavyweight for many purposes

Unfortunately:- Often hard to use, understand;- Not easy to scale, even for experts!- Fragile – once working on a complex codebase, often break with changes

lightweight automated testing with adaptation-based programming alex groce, alan fern, jervis pinto,...

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