1 / 19

Testing Games: Randomizing Regression Tests Using Game Theory

Testing Games: Randomizing Regression Tests Using Game Theory. Nupul Kukreja , William G.J. Halfond , Milind Tambe & Manish Jain Annual Research Review April 30, 2014. Outline. Motivation Problem(s) with traditional test scheduling Game Theory and Randomization

kaleb
Télécharger la présentation

Testing Games: Randomizing Regression Tests Using Game Theory

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Testing Games:Randomizing Regression Tests Using Game Theory Nupul Kukreja, William G.J. Halfond, MilindTambe & Manish Jain Annual Research ReviewApril 30, 2014

  2. Outline • Motivation • Problem(s) with traditional test scheduling • Game Theory and Randomization • Modeling software testing as a 2-player game • Evaluation • Conclusion & Future Work

  3. Motivation Regression Suite Dude! Suite XX is not gonna run! Software Size Let’s CODE NOW FIX LATER DEVS The deadline is close too!

  4. Motivating Problem(s) • Existing test case scheduling activities are deterministic • Developers know which test cases will be executed when • Developers can check in insufficiently tested code closer to delivery deadline • High-turn around time for fixing bugs in low priority features • Random test-scheduling helpful but treats each test case as equally important

  5. Software Testing as a 2-Player Game • This tension between software testers and developers can be modeled as a two-player game • We solve the game to answer the following question: • Given an adaptive adversary (developers) and resource constraints (testers) what is the optimum test-scheduling strategy that maximizes the tester’s expected payoff?

  6. Game Theory • Study of strategic decision making among multiple players – corporations, software agents, testers and developers, regular humans etc.,

  7. Two-player “Security” Game 60% 40% Security game assumptions: What is good for one player (+ve payoff) is bad for the other (-ve payoff) Adversary can conduct perfect surveillance and act appropriately i.e., these are simultaneous move games or Stackelberg games

  8. Testing Game *ITC: Insufficiently tested code *PC: Perfect code i.e., 100% tested

  9. Testing Game – Payoffs • Payoffs are either positive or negative • Proportional to the value of the requirement for both, the tester & developer • Payoffs can be derived in many ways: • Directly from requirement priorities • Expert judgment and/or planning poker • Delphi methods • Directly from test-case priorities

  10. Defining Test Requirements • Could be black-box or white-box based • If black-box, TR may correspond to: • Module/component • Method • OR…the requirement as a whole • “We” group test cases by requirements • Each requirement is ‘covered’ by one or more test cases (or suites)

  11. Not All Developers Are The Same • Commonly encountered personality traits • Lazy/sloppy • New Grad • Moderate/Average • Seasoned Developer • Each persona has a probability of “screwing up” i.e., checking in insufficiently tested code • We can compute these probabilities by looking at the team composition

  12. The Testing Game P(sloppy) = 3/10 P(avg) = 5/10 P(seasoned) = 2/10

  13. Solving the Testing Game Probability of schedulingtest case ‘i'

  14. Example Testing Game Create a test case scheduling of ‘m’ test cases by sampling from the above distribution

  15. Evaluation • Large simulation: • 1000 test requirements = 1 Game • 1000 Games randomly generated • Each game played/solved 1000 times over • Payoffs range from [-10,10] • Constraint: Can only schedule/execute 500 test cases • Compared with: • Deterministic test scheduling • Uniform Random test scheduling • Weighted Random test scheduling • Tester-only weights • Tester+developer based weights

  16. Results

  17. Limitations and Threats to Validity • Developers not adversarial • Developers may choose to be sloppy at times with a particular probability • Lack of perfect historical observation for developers • Expected payoffs is mostly a mathematical notation

  18. Conclusion & Future Work • New approach for test case scheduling using Game Theory • Accounts for tester and developer’s payoffs • Randomizing test cases acts as deterrent for developers, for checking in insufficiently tested code • The test case distribution is optimum under resource constraints and maximizes payoff for worst case developer behavior – robust! • Simulation shows positive results and is a first step to analyzing the tester/developer relationship

  19. DEVS Thank you!Questions?

More Related