FP
F. Palomba
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Code smells are patterns in programming code which indicate potential issues with software quality. Previous research resulted in the development of code smell detectors: automated tools which traverse through large quantities of code and return smell detections to software developers.
The Android platform has become very popular over the years but is relatively new in terms of scientific research. This research investigates the detection perfor- mance of existing detectors, that were not designed with Android in mind, on a dataset of Android apps. In addition, multiple smell detectors and source code metrics are combined in a machine learning approach to develop an Android-specific smell detector. ...
The Android platform has become very popular over the years but is relatively new in terms of scientific research. This research investigates the detection perfor- mance of existing detectors, that were not designed with Android in mind, on a dataset of Android apps. In addition, multiple smell detectors and source code metrics are combined in a machine learning approach to develop an Android-specific smell detector. ...
Code smells are patterns in programming code which indicate potential issues with software quality. Previous research resulted in the development of code smell detectors: automated tools which traverse through large quantities of code and return smell detections to software developers.
The Android platform has become very popular over the years but is relatively new in terms of scientific research. This research investigates the detection perfor- mance of existing detectors, that were not designed with Android in mind, on a dataset of Android apps. In addition, multiple smell detectors and source code metrics are combined in a machine learning approach to develop an Android-specific smell detector.
The Android platform has become very popular over the years but is relatively new in terms of scientific research. This research investigates the detection perfor- mance of existing detectors, that were not designed with Android in mind, on a dataset of Android apps. In addition, multiple smell detectors and source code metrics are combined in a machine learning approach to develop an Android-specific smell detector.
Software testing is widely recognised as a crucial activity for the development of modern software systems, since it points out defects and errors that were made during the development. Testing also improves the reliability and consistency of an application.
However, testing behaviour on mobile devices is not easy because of a number of reasons: (i) manual testing of behaviour requires a lot of valuable time of developers, (ii) the problem of covering all possible operating systems, (iii) covering all possible mobile devices, (iv) and frequently releasing new versions of apps is the industry standard. In addition bugs in mobile apps can cause the reputation of the app on the store to decrease and thereby the the number of downloads. Hence the following question is central to our research: can we automate user interface testing for mobile apps and multiple platforms without human interaction?
To overcome the limitation of multi-platform testing, as well as to help non-developers in the creation of tests, we introduce a novel tool that is able to test behaviour on mobile devices by defining expected scenarios. These scenarios consist of simple human readable instructions that are easy to write and understand by non-developers. These scenarios can then be tested against multiple operating systems and devices by simply connecting a device and pressing a button. ...
However, testing behaviour on mobile devices is not easy because of a number of reasons: (i) manual testing of behaviour requires a lot of valuable time of developers, (ii) the problem of covering all possible operating systems, (iii) covering all possible mobile devices, (iv) and frequently releasing new versions of apps is the industry standard. In addition bugs in mobile apps can cause the reputation of the app on the store to decrease and thereby the the number of downloads. Hence the following question is central to our research: can we automate user interface testing for mobile apps and multiple platforms without human interaction?
To overcome the limitation of multi-platform testing, as well as to help non-developers in the creation of tests, we introduce a novel tool that is able to test behaviour on mobile devices by defining expected scenarios. These scenarios consist of simple human readable instructions that are easy to write and understand by non-developers. These scenarios can then be tested against multiple operating systems and devices by simply connecting a device and pressing a button. ...
Software testing is widely recognised as a crucial activity for the development of modern software systems, since it points out defects and errors that were made during the development. Testing also improves the reliability and consistency of an application.
However, testing behaviour on mobile devices is not easy because of a number of reasons: (i) manual testing of behaviour requires a lot of valuable time of developers, (ii) the problem of covering all possible operating systems, (iii) covering all possible mobile devices, (iv) and frequently releasing new versions of apps is the industry standard. In addition bugs in mobile apps can cause the reputation of the app on the store to decrease and thereby the the number of downloads. Hence the following question is central to our research: can we automate user interface testing for mobile apps and multiple platforms without human interaction?
To overcome the limitation of multi-platform testing, as well as to help non-developers in the creation of tests, we introduce a novel tool that is able to test behaviour on mobile devices by defining expected scenarios. These scenarios consist of simple human readable instructions that are easy to write and understand by non-developers. These scenarios can then be tested against multiple operating systems and devices by simply connecting a device and pressing a button.
However, testing behaviour on mobile devices is not easy because of a number of reasons: (i) manual testing of behaviour requires a lot of valuable time of developers, (ii) the problem of covering all possible operating systems, (iii) covering all possible mobile devices, (iv) and frequently releasing new versions of apps is the industry standard. In addition bugs in mobile apps can cause the reputation of the app on the store to decrease and thereby the the number of downloads. Hence the following question is central to our research: can we automate user interface testing for mobile apps and multiple platforms without human interaction?
To overcome the limitation of multi-platform testing, as well as to help non-developers in the creation of tests, we introduce a novel tool that is able to test behaviour on mobile devices by defining expected scenarios. These scenarios consist of simple human readable instructions that are easy to write and understand by non-developers. These scenarios can then be tested against multiple operating systems and devices by simply connecting a device and pressing a button.