We introduce a methodology and research tools for visual exploratory software analysis. VERDICTS combines exploratory testing, tracing, visualization, dynamic discovery and injection of requirements specifications into a live quick-feedback cycle, without recompilation or restart of the system under test. This supports discovery and verification of software dynamic behavior, software comprehension, testing, and locating the defect origin. At its core, VERDICTS allows dynamic evolution and testing of hypotheses about requirements and behavior, by using contracts as automated component verifiers.
We introduce Semantic Mutation Testing as an approach to evaluate concordance of automated verifiers and the functional specifications they represent with respect to existing implementation. Mutation testing has promise, but also has many known issues. In our tests, both black-box and white-box variants of our Semantic Mutation Testing approach performed better than traditional mutation testing as a measure of quality of automated verifiers.
","abstract_html":"<p>We introduce a methodology and research tools for visual exploratory software analysis. VERDICTS combines exploratory testing, tracing, visualization, dynamic discovery and injection of requirements specifications into a live quick-feedback cycle, without recompilation or restart of the system under test. This supports discovery and verification of software dynamic behavior, software comprehension, testing, and locating the defect origin. At its core, VERDICTS allows dynamic evolution and testing of hypotheses about requirements and behavior, by using contracts as automated component verifiers.</p> <p>We introduce Semantic Mutation Testing as an approach to evaluate concordance of automated verifiers and the functional specifications they represent with respect to existing implementation. Mutation testing has promise, but also has many known issues. In our tests, both black-box and white-box variants of our Semantic Mutation Testing approach performed better than traditional mutation testing as a measure of quality of automated verifiers.</p>","abstract_has_math":false,"creators":["Bolazar, Sefik Kanat"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":["James W. Fawcett"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-08-01T07:00:00Z","date_published":"2013-08-01T07:00:00Z","updated_at":"2026-07-24T04:54:51Z","subjects":["Debugging","Design by Contract","Mutation Testing","Requirements Discovery","Software Testing","Software Visualization","Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://surface.syr.edu/eecs_etd/335","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["James W. Fawcett"]},{"key":"dc:creator","label":"Author","values":["Bolazar, Sefik Kanat"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Debugging","Design by Contract","Mutation Testing","Requirements Discovery","Software Testing","Software Visualization","Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://surface.syr.edu/eecs_etd/335"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["We introduce a methodology and research tools for visual exploratory software analysis. VERDICTS combines exploratory testing, tracing, visualization, dynamic discovery and injection of requirements specifications into a live quick-feedback cycle, without recompilation or restart of the system under test. This supports discovery and verification of software dynamic behavior, software comprehension, testing, and locating the defect origin. At its core, VERDICTS allows dynamic evolution and testing of hypotheses about requirements and behavior, by using contracts as automated component verifiers.
We introduce Semantic Mutation Testing as an approach to evaluate concordance of automated verifiers and the functional specifications they represent with respect to existing implementation. Mutation testing has promise, but also has many known issues. In our tests, both black-box and white-box variants of our Semantic Mutation Testing approach performed better than traditional mutation testing as a measure of quality of automated verifiers.
"]},{"key":"dc:title","label":"Title","values":["VERDICTS: Visual Exploratory Requirements Discovery and Injection for Comprehension and Testing of Software"]}]}],"canonical_facts":{"dc:contributor":["James W. Fawcett"],"dc:creator":["Bolazar, Sefik Kanat"],"dc:description.abstract":["We introduce a methodology and research tools for visual exploratory software analysis. VERDICTS combines exploratory testing, tracing, visualization, dynamic discovery and injection of requirements specifications into a live quick-feedback cycle, without recompilation or restart of the system under test. This supports discovery and verification of software dynamic behavior, software comprehension, testing, and locating the defect origin. At its core, VERDICTS allows dynamic evolution and testing of hypotheses about requirements and behavior, by using contracts as automated component verifiers.
We introduce Semantic Mutation Testing as an approach to evaluate concordance of automated verifiers and the functional specifications they represent with respect to existing implementation. Mutation testing has promise, but also has many known issues. In our tests, both black-box and white-box variants of our Semantic Mutation Testing approach performed better than traditional mutation testing as a measure of quality of automated verifiers.
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