Representational Learning Approach for Predicting Developer Expertise Using Eye Movements

The thesis analyzes an existing eye-tracking dataset collected while software developers were solving bug fixing tasks in an open-source system. The analysis is performed using a representational learning approach namely, Multi-layer Perceptron (MLP). The novel aspect of the analysis is the introduction of a new feature engineering method based on the eye-tracking data. This is then used to predict developer expertise on the data. The dataset used in this thesis is inherently more complex because it is collected in a very dynamic environment i.e., the Eclipse IDE using an eye-tracking plugin, iTrace. Previous work in this area only worked on short code snippets that do not represent how developers usually program in a realistic setting. A comparative analysis between representational learning and non-representational learning (Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest) is also presented. The results are obtained from an extensive set of experiments (with an 80/20 training and testing split) which show that representational learning (MLP) works well on our dataset reporting an average higher accuracy of 30% more for all tasks. Furthermore, a state-of-the-art method for feature engineering is proposed to extract features from the eye-tracking data. The average accuracy on all the tasks is 93.4% with a recall of 78.8% and an F1 score of 81.6%. We discuss the implications of these results on the future of automated prediction of developer expertise. Adviser: Bonita Shari

AN EYE TRACKING REPLICATION STUDY OF A RANDOMIZED CONTROLLED TRIAL ON THE EFFECTS OF EMBEDDED COMPUTER LANGUAGE SWITCHING

The use of multiple programming languages (polyglot programming) during software development is common practice in modern software development. However, not much is known about how the use of these different languages affects developer productivity. The study presented in this thesis replicates a randomized controlled trial that investigates the use of multiple languages in the context of database programming tasks. Participants in our study were given coding tasks written in Java and one of three SQL-like embedded languages: plain SQL in strings, Java methods only, a hybrid embedded language that was more similar to Java. In addition to recording the online questionnaire responses and the participants\u27 solutions to the tasks, the participants\u27 eye movements were also recorded using an eye tracker. Eye tracking as a method for software development studies has grown in recent years and allows for finer-grain information about how developers complete programming tasks. Eye tracking data was collected from 31 participants (from both academia and industry) for each of the six programming tasks they completed. Unlike the original study, we were unable to find a significant effect on productivity due to the language used or whether they were a native English speaker. However, we did find the same effect of participant experience on programming productivity which indicates that more experienced programmers are able to complete polyglot programming tasks in a more efficient manner. We also found that all participants looked at the sample code the same percentage of the time for a given task regardless of their experience or language variant they were given. The top level navigation behavior also remained largely unchanged across experience or language variants. We found that professionals performed more transitions in the code between the Java code and method parameters than their novice counterparts. Overall, we found that the level of polyglot programming did not have as significant of an effect as the task itself. The high-level strategy that participants employed appeared similar regardless of language variant they were given. Adviser: Bonita Shari

An Empirical Study on the Classification of Python Language Features Using Eye-Tracking

Python, currently one of the most popular programming languages, is an object-oriented language that also provides language feature support for other programmingparadigms, such as functional and procedural. It is not currently understood howsupport for multiple paradigms affects the ability of developers to comprehend thatcode. Understanding the predominant paradigm in code, and how developers classifythe predominant paradigm, can benefit future research in program comprehension asthe paradigm may factor into how people comprehend that code. Other researchersmay want to look at how the paradigms in the code interact with various code smells.To investigate how developers classify the predominant paradigm in Python code,we performed an empirical study while utilizing an eye-tracker. The goal was tosee if developers gaze at specific language features while classifying the predominantparadigm and debugging code samples. The study includes both qualitative andquantitative data from 29 Python developers, including their gaze fixations during thetasks. We observed that participants seem to confuse the functional and proceduralparadigms, possibly due to confusing terminology used in Python, though they dogaze at specific language features. Overall, participants took more time classifyingfunctional code. The predominant paradigm did not affect their ability to debug code,though they gave lower confidence ratings for functional code. Adviser: Robert Dye

Understanding Eye Gaze Patterns in Code Comprehension

Program comprehension is a sub-field of software engineering that seeks to understand how developers understand programs. Comprehension acts as a starting point for many software engineering tasks such as bug fixing, refactoring, and feature creation. The dissertation presents a series of empirical studies to understand how developers comprehend software in realistic settings. The unique aspect of this work is the use of eye tracking equipment to gather fine-grained detailed information of what developers look at in software artifacts while they perform realistic tasks in an environment familiar to them, namely a context including both the Integrated Development Environment (Eclipse or Visual Studio) and a web browser (Google Chrome). The iTrace eye tracking infrastructure is used for certain eye tracking studies on large code files as it is able to handle page scrolling and context switching. The first study is a classroom-based study on how students actively trained in the classroom understand grouped units of C++ code. Results indicate students made many transitions between lines that were closer together, and were attracted the most to if statements and to a lesser extent assignment code. The second study seeks to understand how developers use Stack Overflow page elements to build summaries of open source project code. Results indicate participants focused more heavily on question and answer text, and the embedded code, more than they did the title, question tags, or votes. The third study presents a larger code summarization study using different information contexts: Stack Overflow, bug repositories and source code. Results show participants tended to visit up to two codebase files in either the combined or isolated codebase session, but visit more bug report pages, and spend longer time on new Stack Overflow pages they visited, when given either these two treatments in isolation. In the combined session, time spent on the one or two codebase files they viewed dominated the session time. Information learned from tracking developers\u27 gaze in these studies can form foundations for developer behavior models, which we hope can later inform recommendations for actions one might take to achieve workflow goals in these settings. Advisor: Bonita Shari

Quality Evaluation of Requirements Models: The Case of Goal Models and Scenarios

Context: Requirements Engineering approaches provide expressive model techniques for requirements elicitation and analysis. Yet, these approaches struggle to manage the quality of their models, causing difficulties in understanding requirements, and increase development costs. The models’ quality should be a permanent concern. Objectives: We propose a mixed-method process for the quantitative evaluation of the quality of requirements models and their modelling activities. We applied the process to goal-oriented (i* 1.0 and iStar 2.0) and scenario-based (ARNE and ALCO use case templates) models, to evaluate their usability in terms of appropriateness recognisability and learnability. We defined (bio)metrics about the models and the way stakeholders interact with them, with the GQM approach. Methods: The (bio)metrics were evaluated through a family of 16 quasi-experiments with a total of 660 participants. They performed creation, modification, understanding, and review tasks on the models. We measured their accuracy, speed, and ease, using metrics of task success, time, and effort, collected with eye-tracking, electroencephalography and electro-dermal activity, and participants’ opinion, through NASA-TLX. We characterised the participants with GenderMag, a method for evaluating usability with a focus on gender-inclusiveness. Results: For i*, participants had better performance and lower effort when using iStar 2.0, and produced models with lower accidental complexity. For use cases, participants had better performance and lower effort when using ALCO. Participants using a textual representation of requirements had higher performance and lower effort. The results were better for ALCO, followed by ARNE, iStar 2.0, and i* 1.0. Participants with a comprehensive information processing and a conservative attitude towards risk (characteristics that are frequently seen in females) took longer to start the tasks but had a higher accuracy. The visual and mental effort was also higher for these participants. Conclusions: A mixed-method process, with (bio)metric measurements, can provide reliable quantitative information about the success and effort of a stakeholder while working on different requirements models’ tasks