Course detail

Automated Testing and Dynamic Analysis

FIT-ATAAcad. year: 2019/2020

Coverage criteria. Control flow graph. Unit testing. Test doubles. Requirement-based testing. Bug localisation. Data-driven testing. Automatic generation of test data. Fuzz testing. Performance testing. Run-time monitoring. Testing of parallel programs. Test management. Reliability of test reports.

Supervisor

Ing. Aleš Smrčka, Ph.D.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Spillner, A., Linz, A., Schaefer, H.: Software Testing Foundations : A Study Guide for the Certified Tester Exam. Rocky Nook Computing. 2014. p. 296. ISBN 9781937538422.
Kaner, C., James, B., Pettichord, B.: Lessons Learned in Software Testing: A Context-Driven Approach. Wiley Computer Publishing, 2002, 286 s., ISBN 0-471-08112-4.
Marick, B.: The Craft Of Software Testing, Subsystem Testing, Prentice Hall PTR, 1995, ISBN 0-13-177411-5.
Myers, G. J., Sandler, C., Badgett, T.: The Art of Software Testing, 3. vydání. John Wiley & Sons, 2011, 256 s., ISBN 978-1118031964.
Farrell-Vinay, P.: Manage Software Testing. Auerbach Publications, 2008, 537 s., ISBN 978-0-8493-9383-9.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Two projects, 15 points each.

Language of instruction

Czech

Work placements

Not applicable.

Aims

To provide an overview of different approaches to software testing. The focus is put on automated software verification. To gain practical skill of tracing the program run and subsystem communication. To gain practical skill of software testing required by a quality assurance analyst.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NADE , any year of study, summer semester, 5 credits, elective
    specialization NBIO , any year of study, summer semester, 5 credits, elective
    specialization NGRI , any year of study, summer semester, 5 credits, elective
    specialization NNET , any year of study, summer semester, 5 credits, elective
    specialization NVIZ , any year of study, summer semester, 5 credits, elective
    specialization NCPS , any year of study, summer semester, 5 credits, elective
    specialization NSEC , any year of study, summer semester, 5 credits, elective
    specialization NEMB , any year of study, summer semester, 5 credits, elective
    specialization NHPC , any year of study, summer semester, 5 credits, elective
    specialization NISD , any year of study, summer semester, 5 credits, elective
    specialization NIDE , any year of study, summer semester, 5 credits, elective
    specialization NISY , any year of study, summer semester, 5 credits, elective
    specialization NMAL , any year of study, summer semester, 5 credits, elective
    specialization NMAT , any year of study, summer semester, 5 credits, elective
    specialization NSEN , any year of study, summer semester, 5 credits, compulsory
    specialization NVER , any year of study, summer semester, 5 credits, compulsory
    specialization NSPE , any year of study, summer semester, 5 credits, elective

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Ing. Aleš Smrčka, Ph.D.

Syllabus

  1. Model-based testing I
    • Control flow graph, Interprocedural CFG.
    • Coverage-driven generation of test cases.
  2. Model-based testing II
    • Automation of unit tests.
    • xUnit test patterns (Mocking).
  3. Test fixture and test doubles.
  4. Requirement based testing.
    • Requirement classification.
    • Traceability.
    • Automation in Behaviour-driven development (BDD).
  5. Bug localization.
  6. Data-driven testing I
    • Combinatorial testing.
    • Test data minimization.
    • API testing.
    • Systematic generation of test data.
    • Mutation testing.
  7. Data-driven testing II
    • Fuzz testing.
  8. Performance testing
    • Performance parameters.
    • Types and processes of performance testing.
  9. Run-time verification I
    • Low-level tracing.
  10. Run-time verification II
    • Test properties, temporal properties, parametric properties.
    • Program instrumentation.
  11. Testing of parallel programs I
    • Concurrency bug classification.
    • Contracts for concurrency.
    • Systematic vs. random testing.
    • Noise injection methods.
  12. Testing of parallel programs II
    • Atomrace and Eraser algorithms.
    • Vector clocks.
    • Fasttrack algorithm.
  13. Test management
    • Test prioritisation.
    • Test suite evaluation. Test flakiness.
    • Test reports. Reliability of reports. Accuracy, Precision, Recall, F1.

Project

26 hours, compulsory

Teacher / Lecturer

Ing. Aleš Smrčka, Ph.D.

Syllabus

  1. Design of automated test suite with knowledge of source code and/or requirements.
  2. Implementation of run-time monitor.

eLearning

eLearning: currently opened course