Christopher Bogart

Can feature design reduce the gender gap in end-user software development environments?

Recent research has begun to report that female end-user programmers are often more reluctant than males to employ features that are useful for testing and debugging. These earlier findings suggest that, unless such features can be changed in some appropriate way, there are likely to be important gender differences in end-user programmerspsila benefits from these features. In this paper, we compare end-user programmerspsila feature usage in an environment that supports end-user debugging, against an extension of the same environment with two features designed to help ameliorate the effects of low self-efficacy. Our results show ways in which these features affect female versus male enduser programmerspsila self-efficacy, attitudes, usage of testing and debugging features, and performance.

Modeling programmer navigation: A head-to-head empirical evaluation of predictive models

Software developers frequently need to perform code maintenance tasks, but doing so requires time-consuming navigation through code. A variety of tools are aimed at easing this navigation by using models to identify places in the code that a developer might want to visit, and then providing shortcuts so that the developer can quickly navigate to those locations. To date, however, only a few of these models have been compared head-to-head to assess their predictive accuracy. In particular, we do not know which models are most accurate overall, which are accurate only in certain circumstances, and whether combining models could enhance accuracy. Therefore, we have conducted an empirical study to evaluate the accuracy of a broad range of models for predicting many different kinds of code navigations in sample maintenance tasks. Overall, we found that models tended to perform best if they took into account how recently a developer has viewed pieces of the code, and if models took into account the spatial proximity of methods within the code. We also found that the accuracy of single-factor models can be improved by combining factors, using a spreading-activation based approach, to produce multi-factor models. Based on these results, we offer concrete guidance about how these models could be used to provide enhanced software development tools that ease the difficulty of navigating through code.

Predicting reuse of end-user web macro scripts

Repositories of code written by end-user programmers are beginning to emerge, but when a piece of code is new or nobody has yet reused it, then current repositories provide users with no information about whether that code might be appropriate for reuse. Addressing this problem requires predicting reusability based on information that exists when a script is created. To provide such a model for web macro scripts, we identified script traits that might plausibly predict reuse, then used IBM CoScripter repository logs to statistically test how well each corresponded to reuse. We then built a machine learning model that combines the useful traits and evaluated how well it can predict four different types of reuse that we saw in the repository logs. Our model was able to predict reuse from a surprisingly small set of traits. It is simple enough to be explained in only 6–11 rules, making it potentially viable for integration in repository search engines for end-user programmers.

Using traits of web macro scripts to predict reuse

To help people find a code that they might want to reuse, repositories of end-user code typically sort scripts by number of downloads, ratings, or other information based on prior uses of the code. However, this information is unavailable when the code is new or when it has not yet been reused. Addressing this problem requires identifying reusable code based solely on information that exists when a script is created. To provide such a model for web macro scripts, we identified script traits that might plausibly predict reuse, then used IBM CoScripter repository logs to statistically test how well each corresponded to actual reuse. These tests confirmed that the traits generally did correspond to higher levels of reuse as anticipated. We then developed a machine learning model that uses these traits as features to predict reuse of macros. Evaluating this model on repository logs showed that its accuracy is comparable to that of existing machine learning models for predicting reuse-but with a much simpler structure. Sensitivity analysis revealed that our model is quite robust; its quality is greatly reduced only when parameters are set to such extreme values that the model becomes inordinately selective. Testing the model with individual traits revealed those that provided the best predictions on their own. Based on these results, we outline opportunities for using our model to improve repositories of end-user code.

Software visualization for end-user programmers: trial period obstacles

Software visualization for end-user programmers is a relatively unexplored opportunity area. There are advances in software visualization research pertinent to this, but the adoption stage has been entirely ignored. In this paper, we focus on a popular facilitator of adoption decisions: the free trial period. We conducted a case study of an end-user programmer (an accountant) in this situation, as she tried out a commercial spreadsheet visualization tool to make an adoption decision. The results have implications for both theory and design, revealing open questions, design opportunities, and strengths and weaknesses of theoretical foundations.

End-user software engineering and distributed cognition

End-user programmers may not be aware of many software engineering practices that would add greater discipline to their efforts, and even if they are aware of them, these practices may seem too costly (in terms of time) to use. Without taking advantage of at least some of these practices, the software these end users create seems likely to continue to be less reliable than it could be. We are working on several ways of lowering both the perceived and actual costs of systematic software engineering practices, and on making their benefits more visible and immediate. Our approach is to leverage the users cognitive effort through the use of distributed cognition, in which the system and user collaboratively work systematically to reason about the program the end user is creating. This paper demonstrates this concept with a few of our past efforts, and then presents three of our current efforts in this direction.

Rhetorical end-user programming

The study of rhetoric has long been a route to empowerment for people, by helping them share their ideas and inspire cooperation from others. This research discusses the possibility of designers of programming environments to piggyback on that success which would allow people without computer science training to leverage their human communication skills more fully when programming.

Characterizing reusability of end-user web macro scripts

Recommendations by software repositories depend on explicit or implicit models for evaluating the quality and relevance of components for programming tasks. As a step toward creating such a model for evaluating end-user web macro scripts, we have identified script characteristics that correspond to the likelihood of script reuse. For example, the likelihood of reuse increases with the number of variables and comments in the script, the number of online forum postings by the script’s author, and the presence of popular keywords in the script’s source code. We discuss possible applications of our results for new recommendation features.