In total, the data comprises 180 million browsing sessions tracked via 11.44 million cookies from 34,170 buyer companies. The model was tested with two experiments using a dataset that combines cookie-based browsing data from 74 B2B seller companies over a period of fourteen months. In this study, we develop a neural content model to match the content that B2B sellers are providing with the type of content that buyers are seeking. The challenge is that sellers have data but do not know how to utilize it. Further, this study methodology can be adapted by researchers to understand other aspects of programming such as implementing, reusing, and maintaining code.īusiness-to-business (B2B) sellers need to enhance content marketing and analytics in an online environment. 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. ![]() We also identified the strategies used by the participants when finding and fixing bugs. Clear cues helped participants to find and fix bugs with ease while fuzzy and elusive cues led to useless foraging. On analyzing the data, we identified three types of cues: clear, fuzzy, and elusive. The programmers completed two debugging tasks using the Yahoo! Pipes web mashup environment. Through the lens of information foraging theory, we analyzed the data from a controlled lab study of eight web-active end-user programmers. 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. To understand the foraging behavior of end-user programmers when debugging, we used information forging theory. ![]() 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. 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. This work identifies seven types of development friction and provides design recommendations that future tools and environments could use to more effectively help developers complete their tasks. Much of this extra work exists due to mismatches between current tools and environments and how developers actually work in practice. This extra work acts as a form of friction, limiting how quickly and directly developers can complete their tasks. When carrying out these low-level actions, developers routinely perform extra work such as locating and integrating resources and adapting their needs to align with the capabilities of the environment. These goals are often not achievable directly in the environment, forcing developers to translate their task into goals and their goals into the low-level actions provided by the environment. Through a controlled user study with 17 subjects and a field study with 10 industrial engineers, we found that developers frequently formulate specific objectives, or goals, on-demand as they encounter new information when progressing through their tasks. We examine how developers use their tools to perform their tasks and the ways in which these tools inhibit development velocity. In this paper, we investigate how existing desktop environments align with and facilitate developers’ needs as they tackle their tasks. Completing these tasks typically requires developers to use multiple tools, spanning multiple applications, within their environment. Given a task description, a developer’s job is to alter the software system in a way that accomplishes the task, usually by fixing a bug or adding a new feature.
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