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Risk based testing and random testing
 

Risk based testing and random testing

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Risk based testing and random testing

Risk based testing and random testing

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    Risk based testing and random testing Risk based testing and random testing Presentation Transcript

    • Risk Based Testing and Random Testing Dr. Himanshu Hora SRMS College of Engineering & Technology Bareilly (INDIA)
    • Risk Based Testing and Random Testing • Use of Risk Analysis and Metrics for Software Testing • Focus Testing to Save Time and Money while maintaining quality • How to develop metrics to manage and organise large test projects
    • The Challenges • • • • Time Constraints Resource Constraints Quality Requirements Risk Factors: – New technology – Lack of knowledge – Lack of experience • Take Control!
    • Risk Analysis and Testing Test Plan Test Item Tree Risk Strategy Risk Identification Testing, Inspection etc. Risk Assessment Matrix: Cost and Probability Risk Mitigation Test Metrics Risk Reporting Risk Prediction
    • Risk Based Testing - Theory • The Formula Re(f) P(f)*C(f) – Re(f) Risk Exposure of function f – P(f) - Probability of a fault in function f – C(f) - Cost related to a fault in function f
    • Risk Based Testing - Approach • Plan: Identify Elements to be Tested – Logical or physical Functions, Modules etc. • Identify Risk Indicators – What is important to predict the probability of faults? • Identify Cost of faults • Identify Critical Elements – I.e. functions, tasks, activities etc. based on Risk Analysis (Indicators and Cost) • Execute: Improve the Test Process and Organization: Schedule and Track
    • Simple Test Metrics • Test Planning – Number of test cases per function – Number of hours testing per function • Progress Tracking – – – – Number of tests planned, executed and completed Number of faults per function Number of hours used for test and fix Estimated to Complete • Probability of faults - Indicators – – – – New functionality Size Complexity Quality of previous phases and documents • Cost of Faults
    • Risk Based Testing - Metrics • Identify Areas with “High Risk Exposure” – Probability and Cost • All functions/modules should be tested to a “minimum level” • “Extra Testing” in areas with high risk exposure • Establish Test Plan and Schedule – Monitor Quality • Number of Faults per function and time – Monitor Progress • Number of hours in test and fix -> ETC
    • Risk Based Testing - Example Ranking the functions based on Risk Exposure The Probability of a Fault The Cost of a Fault C(c) C(s) Re(f) P(f)* 2 Example: Cost Func. Probability New Design Func. Quality Size 5 5 1 Risk Com- Weighted Exp. plexity Averag func. 3 f C( s ) C(c) Avr. Interest Calc. 3 3 3 2 3 3 3 37 111 Close Account 1 3 2 2 2 2 3 31 62 Cust. Profitab. 2 1 1,5 3 3 2 3 41 61,5 Other Probability Factors might include: Function Points, Frequency of Use etc.
    • High Probability Risk Based Testing - Reporting 1 2 3 4 TECHNICAL INTERFACE RISK 10 1 Low Low 440 Low Medium Probability High 510 439 11 2 2 370 369 5 302 Low Low Medium Medium BUSINESS RISK High High Consequence Consequence High
    • Risk Based Testing - Practice Prior to test execution: identify critical transactions 1 Test Execution identifies “bad” transactions “Top-20” 2 Extra Testing: - Additional testing by product specialist - Automated regression testing 3
    • Planning and Progress Tracking Number of Test Cases On-line Test Cases Completed Planned Executed QAed Date Started Planned Actual
    • Progress Indicators - “To be vs. Actual” “To be Retested” vs. “Actually Retested” To Be Fixed Actually fixed To Be Restested, Actually Retested and Rejected Number of Faults • “To be fixed” vs. “Actually fixed” Number of Faults To Be Fixed and Actually Fixed To be retested Act. retested Rejected
    • Progress Indicators - Hours Used Number of hours for finding one fault and for fixing one Hours per Fault for Test and Fix Hours per Fault Online Batch Fix Hours per Fault for Test and Fix Hours per Fault Number of hours for finding one fault and for fixing one Test Fix Test Date
    • “Estimated to Complete” • ETC for system test based on: Hours ETC – Number of hours testing per fault found – Number of hours fixing per fault – Number of faults found Calculated ETC and Actual Hours per function Actual to Complete – Number of at Time t fixes being rejected – Number of remaining tests (functions to be tested) Date Estimated to Complete at Time t
    • Benefits of Risk Based Testing • Improved Quality? – all critical functions tested • Reduced Time and Money in Testing – effort not wasted on non critical or low risk functions • Improved customer confidence – due to customer involvement and good reporting and progress tracking
    • Test Process Work Flow Risk Identification Risk Assessment Basic Test Data PD LD Case Quality Standards Test Exec. Procedure Test Case Case Build Procedure Test Completed QC / QA Good Test Exec Risk Mitigation Good/ Bad Bad PTDs Raised Risk Reporting Retest Risk Prediction Problem Mngmnt. Procedure Fix Fix Procedure CR Change Mngmnt. Procedure Regression Test ProAte
    • Summary • Risk Based Test Approach – Focused Testing • Reduced Resources • Improved Quality – Metrics are fundamental • Process and Organization must support the new strategy – Metrics must support the organization and process
    • Random testing – Start off with a practical look, and some useful ideas to get you started on the project: random testing for file systems – Then take a deeper look at the notion of feedback and why it is useful: method for testing OO systems from ICSE a couple of years • Then back out to take a look at the general idea of random testing, if time permits
    • A Little Background – Generate program inputs at random – Drawn from some (possibly changing) probability distribution “Throw darts at the state space, without drawing a bullseye” – May generate the same test (or equivalent tests) many times – Will perform operations no sane human would ever perform
    • Random Testing • Millions of operations and scenarios, automatically generated • Run on fast & inexpensive workstations • Results checked automatically by a reference oracle • Hardware simulation for fault injection and reset simulation (x 100000) A day (& night) of testing (x 100000) (x 100000) (x 100000)
    • The Goals • Randomize early testing (since it is not possible to be exhaustive) – We don’t know where the bugs are Nominal Scenario Tests Randomized Testing
    • The Goals • Make use of desktop hardware for early testing – vs. expensive (sloooow) flight hardware testbeds – Many faults can be exposed without full bit-level hardware simulation
    • The Goals • Automate early testing – Run tests all the time, in the background, while continuing development efforts • Automate test evaluation – Using reference systems for fault detection and diagnosis – Automated test minimization techniques to speed debugging and increase regression test effectiveness • Automate fault injection – Simulate hardware failures in a controlled test environment
    • Random testing • Simulated flash hardware layer allows random fault injection • Most development/early testing can be done on workstations • Lots of available compute power – can cover many system behaviors • Will stress software in ways nominal testing will not
    • Differential Testing • How can we tell if a test succeeds? – POSIX standard for file system operations • IEEE produced, ANSI/ISO recognized standard for file systems • Defines operations and what they should do/return, including nominal and fault behavior File system / POSIX operation Result mkdir (“/eng”, …) mkdir (“/data”, …) creat (“/data/image01”, …) creat (“/eng/fsw/code”, …) mkdir (“/data/telemetry”, …) unlink (“/data/image01”) SUCCESS SUCCESS SUCCESS ENOENT SUCCESS SUCCESS /eng /data image01 /telemetry
    • Differential Testing • How can we tell if a test succeeds? – The POSIX standard specifies (mostly) what correct behavior is – We have heavily tested implementations of the POSIX standard in every flavor of UNIX, readily available to us – We can use UNIX file systems (ext3fs, tmpfs, etc.) as reference systems to verify the correct behavior of flash – First differential approach (published) was McKeeman’s testing for compilers
    • Random Differential Testing Choose (POSIX) operation F Perform F on NVFS Perform F on Reference (if applicable) Compare return values Compare error codes Compare file systems Check invariants (inject a fault?)
    • Don’t Use Random Testing for Everything! • Why not test handing read a null pointer? – Because (assuming the code is correct) it guarantees some portion of test operations will not induce failure – But if the code is incorrect, it’s easier and more efficient to write a single test – The file system state doesn’t have any impact (we hope!) on whether there is a null check for the buffer passed to read • But we have to remember to actually do these non-random fixed tests, or we may miss critical, easy-to-find bugs!
    • Principles Used • • • • Random testing (with feedback) Test automation Hardware simulation & fault injection Use of a well-tested reference implementation as oracle (differential testing) • Automatic test minimization (delta-debugging) • Design for testability – Assertions – Downward scalability (small model property) – Preference for predictability
    • Synopsis • Random testing is sometimes a powerful method and could likely be applied more broadly in other missions – Already applied to four file system-related development efforts – Part or all of this approach is applicable to other critical components (esp. with better models to use as references)
    • Thank You Dr. Himanshu Hora SRMS College of Engineering & Technology Bareilly (INDIA)