Automatically identifying valid API versions for software development tutorials on the Web

Abstract

Online tutorials are a valuable source of community‐created information used by numerous developers to learn new APIs and techniques. Once written, tutorials are rarely actively curated and can become dated over time. Tutorials often reference APIs that change rapidly, and deprecated classes, methods, and fields can render tutorials inapplicable to newer releases of the API. Newer tutorials may not be compatible with older APIs that are still in use. In this paper, we first empirically study the tutorial versioning problem, confirming its presence in popular tutorials on the Web. We subsequently propose a technique, based on similar techniques in the literature, for automatically detecting the applicable API version ranges of tutorials, given access to the official API documentation they reference. The proposed technique identifies each API mention in a tutorial and maps the mention to the corresponding API element in the official documentation. The version of the tutorial is determined by combining the version ranges of all of the constituent API mentions. Our technique's precision varies from 61% to 89% and recall varies from 42% to 84% based on different levels of granularity of API mentions and different problem constraints. We observe API methods are the most challenging to accurately disambiguate due to method overloading. As the API mentions in tutorials are often redundant, and each mention of a specific API element commonly occurs several times in a tutorial, the distance of the predicted version range from the true version range is low: 3.61 on average for the tutorials in our sample.

Tutorial mismatches: understanding the frictions due to interface differences when following software video tutorials

“…across mobile and desktop), but this was a design-driven exploration focused on interaction paradigms. We are rather interested in understanding the frictions due to differences in versions or customizations between a tutorial and the interface users have at hand, since they can generate depreciation or a lack of relevance [62,93].…”

Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers

“…Agglomerative clustering uses a combination of (i) a linkage method [15,16] and (ii) a distance metric to merge the clusters. In our analysis, we have used the metrics Euclidean [17], Manhattan [18], and Cosine [19] Hierarchical clustering has important advantages, such as having a logical structure, setting the number of clusters is not required in advance, it provides good result visualization, and it provides dendrogram-based graphical representation [14,20].…”

“…Agglomerative clustering uses a combination of (i) a linkage method [ 15 , 16 ] and (ii) a distance metric to merge the clusters. In our analysis, we used the metrics Euclidean [ 17 ], Manhattan [ 18 ], and Cosine [ 19 ], as well as the following linkage methods: Ward’s method. It links clusters based on the same function as the K-means (Euclidean distance).…”