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An approach to automating mobile application testing on Symbian Smartphones : Functional testing through log file analysis of test cases developed from use casesFärnlycke, Isak January 2013 (has links)
Many developers today have difficulties testing their applications on mobile devices. This is due to a number of factors, such as the fact that the mobile phone market has become even more fragmented with the introduction of touch screen technology. Existing software that was designed for traditional mobile handhelds is not necessarily compatible with the newest models and vice versa. For developers this incompatibility increases the difficulty when creating software. Lack of resources for testing the application may lead to the application being limited to either just a specific model or in some cases to only one specific version of the operating system software. Without providing support for a large number of models the product may have difficulty attracting customers, and hence fail to gain the desired market share. The challenge is to find a way to make testing simple, effective, and automated on a large number of mobile devices. To achieve this test automation applicationsare needed and a test strategy must be devised. Additionally, testing is often described as never-ending since testing generally reveals errors rather than demonstrating when errors are absent. Because of this some limitations of testing are justified. In order to limit the scope of this thesis I have selected some of the most appropriate methods for testing, and will only examine these specific methods. The focus for the testing is not specifically to find errors, but rather to confirm that the product offers the specified functionality. This thesis describes an approach to functional testing of an application for Symbian mobile devices based upon log analysis. Unfortunately, testing applications on mobile devices is still not straightforward, and this thesis does not shed any light upon how to lessen this complexity. However, I believe that both testing and development will be more and more built around use cases in the future. Unfortunately, automation of testing based uponthese use cases will be further complicated by the increasing use of touch screens and physical input (such as gestures). / Idag har många utvecklare problem med att testa sina applikationer på mobila enheter.Detta har många orsaker, exempelvis att den globala mobila marknaden har blivit än mer fragmenterad i och med introduktionen av pekskärmstekniken och de snabba förändringar som sker idag. På grund av de många telefoner som idag finns så finns det ett behov för en automatiserad testprocess då det tar för lång tid att göra manuellt. OptiCall Solutions AB har utvecklat en applikation för Symbian S60 som behöver kunna köra på många olika telefoner. Denna masteruppsats har målet att hitta ett sätt att automatisera testning av mobilapplikationer på olika enheter, mer specifikt enheter som kör Symbian S60. OptiCaller är målet för testerna. Testmetodologier och verktyg har analyserats och kraven har samlats in på den önskade lösningen. Lösningen består av ett program som kör testskripten direkt på telefonen, mjukvara som analyserar testresultaten och presenterar dem i ett GUI, ett teststrategidokument, samt ett felrapporteringssystem. Med hjälp av dess kan testaren skapa sina egna skript för att automatisera och sedan samla in resultaten för analys. Detta eliminerar behovet för manuell testning och gör testningen effektivare, speciellt när man kör många tester. Analysmjukvaran är även integrerad med Felrapporteringssystemet för att underlätta felrapportering.
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Tuning of machine learning algorithms for automatic bug assignmentArtchounin, Daniel January 2017 (has links)
In software development projects, bug triage consists mainly of assigning bug reports to software developers or teams (depending on the project). The partial or total automation of this task would have a positive economic impact on many software projects. This thesis introduces a systematic four-step method to find some of the best configurations of several machine learning algorithms intending to solve the automatic bug assignment problem. These four steps are respectively used to select a combination of pre-processing techniques, a bug report representation, a potential feature selection technique and to tune several classifiers. The aforementioned method has been applied on three software projects: 66 066 bug reports of a proprietary project, 24 450 bug reports of Eclipse JDT and 30 358 bug reports of Mozilla Firefox. 619 configurations have been applied and compared on each of these three projects. In production, using the approach introduced in this work on the bug reports of the proprietary project would have increased the accuracy by up to 16.64 percentage points.
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