Mobile Application Testing and Assessment

Jeleński, Marcin J.; Sosnowski, J.

doi:10.1007/978-3-030-48256-5_28

Cited by 1 publication

(4 citation statements)

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“…Special metrics are used to assess test quality. Most popular include branch and path coverage, decision/condition coverage, program mutation coverage [1] [2] [35] [36], and program version coverage [37]. They may suggest program components not sufficiently stressed by the test suite.…”

Section: B Test Assessmentmentioning

confidence: 99%

“…This can be enhanced with the operational profile coverage expressed by application driven features (compare Section IV and V). In [37] test coverage is also considered in relevance to program versions. Test diagnosability evaluation needs deeper analysis.…”

Section: B Test Assessmentmentioning

confidence: 99%

“…This additionally confirms negligence of the pointed problems in correlation with testing, which we found as important practical issues needing investigation. [2, 5, 8, 12 -14, 34-36, 44] Testing project performance [16,17] Bug classification [17, 24-26, 28, 29] Test quality assessment [1 -4, 6, 10, 11, 15, 21, 34-37] Fault localization and program diagnosability [3,22,23,38,39] Test optimization and prioritization [18 -23, 42, 43] Logging schemes [9,28,30] Log analysis and parsing [9, 30 -32] Anomaly detection [9,33,34] Log statement optimization [9,34] Issue and version control tracking [7,25,30,37] Bug triaging [27] The created test scenarios are upgraded during the development and maintenance of the program. Moreover, in a large extent they are used during regression testing in relevance to function upgrades, extensions, or corrections.…”

Section: B Test Assessmentmentioning

confidence: 99%

“…We can derive positive correlations of project actors with test subsets (successful and fast handling). In the case of projects with recommendation repositories filled by users [37] we can correlate them with project functionalities and relevant test suites to check why there were not detected during development/maintenance phase, and specify, if needed, test drawbacks.…”

Section: B Correlating Repository Datamentioning

confidence: 99%

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Extending the Space of Software Test Monitoring: Practical Experience

Lasynskyi

Sosnowski

2021

IEEE Access

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Software reliability depends on the performed tests. Bug detection and diagnosis are based on test outcome (oracle) analysis. Most practical test reports do not provide sufficient information for localizing and correcting bugs. We have found the need to extend the space of test result observation in data and time perspectives. This resulted in tracing supplementary test result features in event logs. They are explored with combined text mining and log parsing techniques. Another important point is correlating test life cycle with project development history journaled in issue tracking and software version control repositories. Dealing with the outlined problems, neglected in the literature, we have introduced original analysis schemes. They focus on assessing test coverage, reasons of low diagnosability, and test result profiles. Multidimensional investigation of test features and their management is supported with the developed test infrastructure. This assures a holistic insight into the test efficiency to identify test scheme deficiencies (e.g., functional inadequacy, aging, insufficient coverage) and possible improvements (test set updates). Our studies have been verified in relevance to a real commercial project (industrial case study) and confronted with the experience of testers engaged in other projects.INDEX TERMS bug diagnostics, software defect repository, software testing, testing framework, test monitoring.

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