![]() ![]() Compared to the existing observation-based backward slicing technique, ORBS, MOAD requires only 18.6% of the observations, while the resulting slices are only 12% larger on average. ![]() We evaluate MOAD using program slices obtained from the resulting probabilistic dependency models. MOAD thus infers a model of program dependency that captures the relationship between the modification and observation points. MOAD generates program variants by deleting parts of the source code and executing them while observing the impact. Our technique, MOAD (Modeling Observation-based Approximate Dependency), reformulates program dependency as the likelihood that one program element is dependent on another (instead of a Boolean relationship). We present a novel dependency analysis technique that aims to approximate program dependency from a relatively small number of perturbed executions. While dependency analysis is foundational to much program analysis, many techniques have limited scalability and handle only monolingual systems. Finally, we found that,in terms of the activities developers did, programming and debugging were remarkably similar, particularly in the frequency of editing and browsing code. We found no single activity that dominated debugging time, and long debugging episodes often involved many diverse activities. However, most debugging time is spent in long debugging episodes. Debugging episodes vary greatly in time, with most being less than a few minutes and a few as more than 100 minutes. We found that debugging was frequent, even in programming work, occurring once every eight minutes. We then systematically coded the debugging episodes and activities that occurred within these videos, yielding a dataset of 2137 debugging activities and 1407 programming activities. ![]() #Coupon code for pdfsam professional#Using this data source, we curated 15 sessions in which 11 professional developers worked for 30 hours. We observed developers by watching professional developers at work in live-streamed programming sessions. We investigate the typical duration and frequency of debugging episodes and the typical activities which occur. In this study, we focus on characterizing debugging episodes from the moment at which developers first encounter a defect to the moment at which it is resolved. However, less is known about the typical progression of debugging in real world settings. Many studies have long investigated how developers debug, shaping our understanding of debugging and helping motivate the creation of more effective tools. ![]()
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