One of the most common errors developers make is to provide incorrect string

identifiers across the HTML5-JavaScript-CSS3 stack. The existing literature shows that a

significant percentage of defects observed in real-world codebases belong to this

category. Existing work focuses on semantic static analysis, while this thesis attempts to

tackle the challenges that can be solved using syntactic static analysis. This thesis

proposes a tool for quickly identifying defects at the time of injection due to

dependencies between HTML5, JavaScript, and CSS3, specifically in syntactic errors in

string identifiers. The proposed solution reduces the delta (time) between defect injection

and defect discovery with the use of a dedicated just-in-time syntactic string identifier

resolution tool. The solution focuses on modeling the nature of syntactic dependencies

across the stack, and providing a tool that helps developers discover such dependencies.

This thesis reports on an empirical study of the tool usage by developers in a realistic

scenario, with the focus on defect injection and defect discovery times of defects of this

nature (syntactic errors in string identifiers) with and without the use of the proposed

tool. Further, the tool was validated against a set of real-world codebases to analyze the

significance of these defects.

Mobile data collection (MDC) applications have been growing in the last decade

especially in the field of education and research. Although many MDC applications are

available, almost all of them are tailor-made for a very specific task in a very specific

field (i.e. health, traffic, weather forecasts, …etc.). Since the main users of these apps are

researchers, physicians or generally data collectors, it can be extremely challenging for

them to make adjustments or modifications to these applications given that they have

limited or no technical background in coding. Another common issue with MDC

applications is that its functionalities are limited only to data collection and storing. Other

functionalities such as data visualizations, data sharing, data synchronization and/or data updating are rarely found in MDC apps.

This thesis tries to solve the problems mentioned above by adding the following

two enhancements: (a) the ability for data collectors to customize their own applications

based on the project they’re working on, (b) and introducing new tools that would help

manage the collected data. This will be achieved by creating a Java standalone

application where data collectors can use to design their own mobile apps in a userfriendly Graphical User Interface (GUI). Once the app has been completely designed

using the Java tool, a new iOS mobile application would be automatically generated

based on the user’s input. By using this tool, researchers now are able to create mobile

applications that are completely tailored to their needs, in addition to enjoying new

features such as visualize and analyze data, synchronize data to the remote database,

share data with other data collectors and update existing data.