Developers are often tasked with maintaining complex systems. Regardless of prior experience, there will inevitably be times in which they must interact with parts of the system with which they are unfamiliar. In such cases, recommendation systems may serve as a valuable tool to assist the developer in implementing a solution. Many recommendation systems in software engineering utilize the Stack Overflow knowledge-base as the basis of forming their recommendations. Traditionally, these systems have relied on the developer to explicitly invoke them, typically in the form of specifying a query. However, there may be cases in which the developer is in need of a recommendation but unaware that their need exists. A new class of recommendation systems deemed Behavior-Driven Recommendation Systems for Software Engineering seeks to address this issue by relying on developer behavior to determine when a recommendation is needed, and once such a determination is made, formulate a search query based on the software engineering task context. This thesis presents one such system, StackInTheFlow, a plug-in integrating into the IntelliJ family of Java IDEs. StackInTheFlow allows the user to intervi act with it as a traditional recommendation system, manually specifying queries and browsing returned Stack Overflow posts. However, it also provides facilities for detecting when the developer is in need of a recommendation, defined when the developer has encountered an error messages or a difficulty detection model based on indicators of developer progress is fired. Once such a determination has been made, a query formulation model constructed based on a periodic data dump of Stack Overflow posts will automatically form a query from the software engineering task context extracted from source code currently open within the IDE. StackInTheFlow also provides mechanisms to personalize, over time, the results displayed to a specific set of Stack Overflow tags based on the results previously selected by the user. The effectiveness of these mechanisms are examined and results based the collection of anonymous user logs and a small scale study are presented. Based on the results of these evaluations, it was found that some of the queries issued by the tool are effective, however there are limitations regarding the extraction of the appropriate context of the software engineering task yet to overcome.

With increasing demand placed on online accessibility, a tool that enables developers to more easily build accessible websites for colour vision deficient (CVD) internet users becomes a crucial consideration. An extension was developed for the web browser, Google Chrome, and issues for CVD internet users were identified. The identification was based on the luminance and colour contrast between different objects next to each other on a web page, such as fonts and their background. The extension calculated how a CVD internet user would experience the colours and then checked the contrast between them. The extension's calculations and reliability was assessed through a evaluation with CVD internet users and the results suggested that it would be possible to reliably detect issues related to CVD internet users with a algorithm implemented in a Chrome extension. / Med ökande krav på tillgänglighet på internet så ökar behovet av ett verktyg som underlättar för utvecklare att bygga hemsidor för personer med defekt färgseende. Ett tilläggsprogram utvecklades för webbläsaren Google Chrome, och problem för personer med defekt färgseende identifierades. Identifikationen baserades på luminanskontrast och färgkontrast mellan olika närliggande objekt på en hemsida, så som fonter och deras bakgrund. Tilläggsprogrammet beräknade hur en person med defekt färgseende upplever färgerna på hemsidan och kontrollerar kontrasten mellan dem. Tilläggsprogrammets tillförlitlighet bedömdes med hjälp av en utvärdering genomförd med personer med defekt färgseende. Utvärderingen indikerade att det är möjligt att tillförlitligt identifiera problem relaterade till personer med defekt färgseende med hjälp av en algoritm implementerad i ett Chrome tilläggsprogram.