Incremental Control Dependency Frontier Exploration for Many-Criteria Test Case Generation

Annibale Panichella, Fitsum Meshesha Kifetew, Paolo Tonella

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

12 Citations (Scopus)
231 Downloads (Pure)


Several criteria have been proposed over the years for measuring test suite adequacy. Each criterion can be converted into a specic objective function to optimize with search-based techniques in an attempt to generate test suites achieving the highest possible coverage for that criterion. Recent work has tried to optimize for multiple-criteria at once by constructing a single objective function obtained as a weighted sum of the objective functions of the respective criteria. However, this solution suers the problem of sum scalarization, i.e., dierences along the various dimensions being optimized get lost when such dimensions are projected into a single value. Recent advances in SBST formulated coverage as a many-objective optimization problem rather than applying sum scalarization. Starting from this formulation, in this work, we apply many-objective test generation that handles multiple adequacy criteria simultaneously. To scale the approach to the big number of objectives to be optimized at the same time, we adopt an incremental strategy, where only coverage targets in the control dependency frontier are considered until the frontier is expanded by covering a previously uncovered target.
Original languageEnglish
Title of host publicationSearch-Baed Software Engineering - 10th International Symposium, SSBSE 2018 - Proceedings
EditorsThelma Elita Colanzi, Phil McMinn
Place of PublicationCham
Number of pages16
ISBN (Electronic)978-3-319-99241-9
ISBN (Print)78-3-319-99240-2
Publication statusPublished - 3 Sept 2018
EventSSBSE 2018: 10th International Symposium on Search-Based Software Engineering - Montpellier, France
Duration: 8 Sept 201810 Sept 2018
Conference number: 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11036 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceSSBSE 2018
Abbreviated titleSSBSE
Internet address

Bibliographical note

Accepted Author Manuscript


  • Test case generation
  • Evolutionary computation
  • Code coverage


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