The goal of this thesis is to learn how exception handling constructs are used by PHP developers. We present an approach for detecting the exception flow of a software system, based on the work of Robillard and Murphy (2003). We show the accuracy of this approach by evaluating the tool on a corpus of three different PHP systems. The approach is thereafter used to perform an empirical study on a corpus of 20 PHP systems. For each of these systems, we compute the exception flow and measure the number of exceptions that are encountered, how often exceptions are propagated before they are caught, by what type they are typically caught, and whether they are documented. The results show that many exceptions are propagated often before they are caught and that many are caught by subsumption. Another finding is that exceptions are often not documented, which in many cases is a violation of the Liskov Substitution Principle.

Software testing is an important, well-researched field.
With the majority of modern-day applications using relational databases to manipulate their data, it is crucial that database interactions are tested as well.
This is a complex task to perform manually, and thus researchers have been attempting to tackle this problem by means of automated test data generation.
In their studies, they apply constraint-based techniques using SAT solvers to generate the test data.
However, these techniques have known limitations such as solving subqueries.

In this thesis, we present a novel search-based approach that uses a Genetic Algorithm to generate test data for SQL queries, which overcomes the limitations of previous research.
We provide an implementation of our approach, EvoSQL.
In our implementation, we instrument a real database to extract all the information necessary for the fitness function. By doing so, we support all queries using standard SQL syntax.
We evaluate our approach on 2,135 queries from 4 real-world systems, of which EvoSQL is able to cover over 96% fully.