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Checking software application suitability using automated software tools hasbecome a vital element for most organisations irrespective of whether theyproduce in-house software or simply customise off-the-shelf softwareapplications for internal use. As software solutions become ever more complex,the industry becomes increasingly dependent on software automation tools, yetthe brittle nature of the available software automation tools limits theireffectiveness. Companies invest significantly in obtaining and implementingautomation software but most of the tools fail to deliver when the cost ofmaintaining an effective automation test suite exceeds the cost and time thatwould have otherwise been spent on manual testing. A failing in the currentgeneration of software automation tools is they do not adapt to unexpectedmodifications and obstructions without frequent (and time expensive) manualinterference. Such issues are commonly acknowledged amongst industrypractitioners, yet none of the current generation of tools have leveraged theadvances in machine learning and artificial intelligence to address theseproblems.  This paper proposes a framework solution that utilises machine learningconcepts, namely fuzzy matching and error recovery. The suggested solutionapplies adaptive techniques to recover from unexpected obstructions that wouldotherwise have prevented the script from proceeding. Recovery details arepresented to the user in a report which can be analysed to determine if therecovery procedure was acceptable and the framework will adapt future runsbased on the decisions of the user. Using this framework, a practitioner canrun the automated suits without human intervention while minimising the risk ofschedule delays.

Adaptive Automation: Leveraging Machine Learning to Support Uninterrupted Automated Testing of Software Applications