Readers of programs have two main sources of domain information: identifier names and comments.When functions are uncommented, as many are, comprehension is almost exclusively dependent on the identifier names. Assuming that writers of programs want to create quality identifiers (e.g., include relevant domain knowledge) how should they go about it? For example, do the initials of a concept name provide enough information to represent the concept? If not, and a longer identifier is needed, is an abbreviation satisfactory or does the concept need to be captured in an identifier that includes full words?Results from a study designed to investigate these questions are reported. The study involved over 100 programmers who were asked to describe twelve different functions. The functions used three different "levels" of identifiers: single letters, abbreviations, and full words. Responses allow the level of comprehension associated with the different levels to be studied.The functions include standard algorithms studied in computer science courses as well as functions extracted from production code. The results show that full word identifiers lead to the best comprehension; however, in many cases, there is no statistical difference between full words and abbreviations.

When search engine users have trouble finding information, they may become frustrated, possibly resulting in a bad experience (even if they are ultimately successful). In a user study in which participants were given difficult information seeking tasks, half of all queries submitted resulted in some degree of self-reported frustration. A third of all successful tasks involved at least one instance of frustration. By modeling searcher frustration, search engines can predict the current state of user frustration and decide when to intervene with alternative search strategies to prevent the user from becoming more frustrated, giving up, or switching to another search engine. We present several models to predict frustration using features extracted from query logs and physical sensors. We are able to predict frustration with a mean average precision of 66% from the physical sensors, and 87% from the query log features.

Readers of programs have two main sources of domain information: identifier names and comments. When functions are uncommented, as many are, comprehension is almost exclusively dependent on the identifier names. Assuming that writers of programs want to create quality identifiers (e.g., identifiers that include relevant domain knowledge), one must ask how should they go about it. For example, do the initials of a concept name provide enough information to represent the concept? If not, and a longer identifier is needed, is an abbreviation satisfactory or does the concept need to be captured in an identifier that includes full words? What is the effect of longer identifiers on limited short term memory capacity? Results from a study designed to investigate these questions are reported. The study involved over 100 programmers who were asked to describe 12 different functions and then recall identifiers that appeared in each function. The functions used three different levels of identifiers: single letters, abbreviations, and full words. Responses allow the extent of comprehension associated with the different levels to be studied along with their impact on memory. The functions used in the study include standard computer science textbook algorithms and functions extracted from production code. The results show that full-word identifi-

Identifiers, which represent the defined concepts in a program, account for, by some measures, almost three quarters of source code. The makeup of identifiers plays a key role in how well they communicate these defined concepts. An empirical study of identifier quality based on almost 50 million lines of code, covering thirty years, four programming languages, and both open and proprietary source is presented. For the purposes of the study, identifier quality is conservatively defined as the possibility of constructing the identifier out of dictionary words or known abbreviations. Four hypotheses related to identifier quality are considered using linear mixed effect regression models. For example, the first hypothesis is that modern programs include higher quality identifiers than older ones. In this case, the results show that better programming practices are producing higher quality identifies. Results also confirm some commonly held beliefs, such as proprietary code having more acronyms than open source code.

When generating query recommendations for a user, a natural approach is to try and leverage not only the user's most recently submitted query, or reference query, but also information about the current search context, such as the user's recent search interactions. We focus on two important classes of queries that make up search contexts: those that address the same information need as the reference query (on-task queries), and those that do not (off-task queries). We analyze the effects on query recommendation performance of using contexts consisting of only on-task queries, only off-task queries, and a mix of the two. Using TREC Session Track data for simulations, we demonstrate that ontask context is helpful on average but can be easily overwhelmed when off-task queries are interleaved-a common situation according to several analyses of commercial search logs. To minimize the impact of off-task queries on recommendation performance, we consider automatic methods of identifying such queries using a state of the art search task identification technique. Our experimental results show that automatic search task identification can eliminate the effect of off-task queries in a mixed context.We also introduce a novel generalized model for generating recommendations over a search context. While we only consider query text in this study, the model can handle integration over arbitrary user search behavior, such as page visits, dwell times, and query abandonment. In addition, it can be used for other types of recommendation, including personalized web search.