**4. Information Foraging Theory and Yahoo! Pipes**

Information Foraging Theory (IFT) was developed by Pirolli and Card [11] to understand how people search for information. IFT was inspired by optimal foraging theory, which is a biological theory explaining how predators hunt for their prey in the wild. Optimal foraging theory predicts whether a prey (animal) will try to maximize the energy it gains or minimize the expense to obtain a fixed amount of energy [12]. Similarly, while foraging for information, users must realize their maximum return on information gain at minimum expenditure of their time. Therefore, users, when possible, will modify their strategies to maximize their rate of gaining valuable information [13]. **Table 1** elaborates the IFT terminologies along with examples from Yahoo! Pipes.

IFT has helped to improve the understanding of the users' behaviors and interactions on the web. In the very beginning, research was done for general Internet users, which led to the foundation of IFT [15, 18, 51]. Research has been done to observe and study foragers on the web [8, 15, 21, 51]. IFT has been used to improve the usability of web sites [52] as it has helped to explain and predict why people click a particular link, text, or button on a website [14]. In this research, we qualitatively analyze multiple end-user's foraging behavior to find solutions for their bugs on the web.

IFT has also been used to understand software engineering and software development [8, 19, 20] along with its collaborative environments [17]. Piorkowski et al. have explored foraging behavior and the difference in foraging between desktop and mobile integrated development environment (IDE) [53]. Niu et al. used IFT to design navigation affordances in IDEs [54]. Similarly, IFT has been used to find out the optimal team size for open-source projects [55]. IFT can help to understand the foraging behavior of web-active end-user programmers when engaged in programming activities such as comprehension, reusage of code, implementation, debugging and testing. This research focuses on the debugging behavior of web-active end-user programmers.

Researchers have built computational models of user information foraging behavior when completing tasks [14, 56, 57]. These models have also helped in predicting the effects of social influences on IFT [58]. The researchers have developed


#### **Table 1.**

*IFT Terminologies from the Yahoo! Pipes Perspective [2].*

*How Do Web-Active End-User Programmers Forage? DOI: http://dx.doi.org/10.5772/intechopen.97765*

the WUFIS model for the web [6] and the PFIS model for programmers foraging in IDEs [19, 20]. Ragavan et al. analyzed the novice programmers' foraging in the presence of program variants [22] and built a predictive model [59] inspired by the PFIS model [23, 60]. Our focus is to understand the end-user foraging behavior before creating such computational models.
