How Do Web-Active End-User Programmers Forage?

Web-active end-user programmers spend substantial time and cognitive effort seeking information while debugging web mashups, which are platforms for creating web applications by combining data and functionality from two or more different sources. The debugging on these platforms is challenging as end user programmers need to forage within the mashup environment to find bugs and on the web to forage for the solution to those bugs. To understand the foraging behavior of end-user programmers when debugging, we used information forging theory. Information foraging theory helps understand how users forage for information and has been successfully used to understand and model user behavior when foraging through documents, the web, user interfaces, and programming environments. Through the lens of information foraging theory, we analyzed the data from a controlled lab study of eight web-active end-user programmers. The programmers completed two debugging tasks using the Yahoo! Pipes web mashup environment. On analyzing the data, we identified three types of cues: clear, fuzzy, and elusive. Clear cues helped participants to find and fix bugs with ease while fuzzy and elusive cues led to useless foraging. We also identified the strategies used by the participants when finding and fixing bugs. Our results give us a better understanding of the programming behavior of web-active end-users and can inform researchers and professionals how to create better support for the debugging process. Further, this study methodology can be adapted by researchers to understand other aspects of programming such as implementing, reusing, and maintaining code

Information Foraging Theory as a Unifying Foundation for Software Engineering Research : Connecting the Dots

Empirical studies have shown that programmers spend up to one-third of their time navigating through code during debugging. Although researchers have conducted empirical studies to understand programmers’ navigation difficulties and developed tools to address those difficulties, the resulting findings tend to be loosely connected to each other. To address this gap, we propose using theory to “connect the dots” between software engineering (SE) research findings. Our theory of choice is Information Foraging Theory (IFT) which explains and predicts how people seek information in an environment. Thus, it is well-suited as a unifying foundation because navigating code is a fundamental aspect of software engineering. In this dissertation, we investigated IFT’s suitability as a unifying foundation for SE through a combination of tool building and empirical user studies of programmers debugging. Our contributions show how IFT can help to unify SE research via cross-cutting insights spanning multiple software engineering subdisciplines

Coding Theory

This book explores the latest developments, methods, approaches, and applications of coding theory in a wide variety of fields and endeavors. It consists of seven chapters that address such topics as applications of coding theory in networking and cryptography, wireless sensor nodes in wireless body area networks, the construction of linear codes, and more

Variations Foraging

Information Foraging Theory (IFT) has successfully explained how people seek information in various domains, in turn, informing the design of several tools and information-intensive environments. However, prior research has not explored foraging in the presence of several, very similar variants of the same artifact. Such variants are commonplace in several creative, exploratory tasks such as graphic design, writing and programming. In this thesis, we evaluate whether and how IFT applies to variants. Using empirical studies and computational models that predict programmers’ information foraging among variants, this thesis provides evidence for the applicability of IFT in variations situations and offers new insights for variations-support tools. Along the way, this thesis also demonstrates the benefits of computationally modeling: 1) the hierarchical organization of information environments, 2) variable costs of navigation in an information environment and 3) accounting for non-textual (graphical) information

Toward an Effective Automated Tracing Process

Traceability is defined as the ability to establish, record, and maintain dependency relations among various software artifacts in a software system, in both a forwards and backwards direction, throughout the multiple phases of the project’s life cycle. The availability of traceability information has been proven vital to several software engineering activities such as program comprehension, impact analysis, feature location, software reuse, and verification and validation (V&V). The research on automated software traceability has noticeably advanced in the past few years. Various methodologies and tools have been proposed in the literature to provide automatic support for establishing and maintaining traceability information in software systems. This movement is motivated by the increasing attention traceability has been receiving as a critical element of any rigorous software development process. However, despite these major advances, traceability implementation and use is still not pervasive in industry. In particular, traceability tools are still far from achieving performance levels that are adequate for practical applications. Such low levels of accuracy require software engineers working with traceability tools to spend a considerable amount of their time verifying the generated traceability information, a process that is often described as tedious, exhaustive, and error-prone. Motivated by these observations, and building upon a growing body of work in this area, in this dissertation we explore several research directions related to enhancing the performance of automated tracing tools and techniques. In particular, our work addresses several issues related to the various aspects of the IR-based automated tracing process, including trace link retrieval, performance enhancement, and the role of the human in the process. Our main objective is to achieve performance levels, in terms of accuracy, efficiency, and usability, that are adequate for practical applications, and ultimately to accomplish a successful technology transfer from research to industry

Find Unique Usages: Helping Developers Understand Common Usages

When working in large and complex codebases, developers face challenges using \textit{Find Usages} to understand how to reuse classes and methods. To better understand these challenges, we conducted a small exploratory study with 4 participants. We found that developers often wasted time reading long lists of similar usages or prematurely focused on a single usage. Based on these findings, we hypothesized that clustering usages by the similarity of their surrounding context might enable developers to more rapidly understand how to use a function. To explore this idea, we designed and implemented \textit{Find Unique Usages}, which extracts usages, computes a diff between pairs of usages, generates similarity scores, and uses these scores to form usage clusters. To evaluate this approach, we conducted a controlled experiment with 12 participants. We found that developers with Find Unique Usages were significantly faster, completing their task in 35% less time

Implications of Computational Cognitive Models for Information Retrieval

This dissertation explores the implications of computational cognitive modeling for information retrieval. The parallel between information retrieval and human memory is that the goal of an information retrieval system is to find the set of documents most relevant to the query whereas the goal for the human memory system is to access the relevance of items stored in memory given a memory probe (Steyvers & Griffiths, 2010). The two major topics of this dissertation are desirability and information scent. Desirability is the context independent probability of an item receiving attention (Recker & Pitkow, 1996). Desirability has been widely utilized in numerous experiments to model the probability that a given memory item would be retrieved (Anderson, 2007). Information scent is a context dependent measure defined as the utility of an information item (Pirolli & Card, 1996b). Information scent has been widely utilized to predict the memory item that would be retrieved given a probe (Anderson, 2007) and to predict the browsing behavior of humans (Pirolli & Card, 1996b). In this dissertation, I proposed the theory that desirability observed in human memory is caused by preferential attachment in networks. Additionally, I showed that documents accessed in large repositories mirror the observed statistical properties in human memory and that these properties can be used to improve document ranking. Finally, I showed that the combination of information scent and desirability improves document ranking over existing well-established approaches