Self-Admitted Technical Debt (SATD) represents the admission, made through source code comments or other channels, of portions of a program being poorly implemented, containing provisional solutions or, in general, simply being not ready yet. To better understand developers' habits in SATD annotation, and possibly support their exploitation in tool support, this paper provides an in-depth analysis of the content provided in SATD comments, and the expressed sentiment. We manually inspect and classify 1038 instances from an existing dataset, grouping them along a taxonomy composed of 41 categories (of which 9 top-level ones), identifying their sentiment, and the presence of external references such as author names or issue IDs. Results of our study indicate that (i) the SATD content is crosscutting along life-cycle dimensions identified in previous work, (ii) comments related to functional problems or on-hold SATD are generally more negative than poor implementation choices or partially implemented functionality, and (iii) despite observations from previous literature, only a minority of SATD comments leverage external references.

Self-Admitted Technical Debt (SATD) are comments, left by developers in the source code or elsewhere, aimed at describing the presence of TD, i.e., source code "not ready yet". Although this was never stated in the original paper by Potdar and Shihab, the term SATD might suggest that it refers to a "self-admission" by whoever has written or changed the source code. This paper empirically investigates, using a curated SATD dataset from five Java open-source projects, (i) the extent to which SATD comments are introduced by authors different from those who have done last changes to the related source code, and (ii) when this happens, what is the level of ownership those developers have about the commented source code. Results of the study indicate that, depending on the project, the percentage of SATD admissions introduced or changed without modifying the related source code varies between 0% and 16%, and therefore represent a small, yet not negligible, phenomenon. The level of ownership of those developers is not particularly low, with a median value per project between 10% and 42%. This indicates the possible use of SATD as a different way to perform code review, although this behavior should be considered sub-optimal to the use of more traditional tools, which entail suitable notification mechanisms.

Computation of node betweenness centrality (BC) of weighted and directed graphs is a time-consuming task that could limit the application of such a metric for monitoring large, dynamic networks. As widely demonstrated in previous work, approximated approaches represent a solution to reduce computation time when ranking nodes according to their BC values is sufficient with respect to knowing their exact BC values. According to this observation, we have proposed a fast algorithm for computing approximated BC values for large weighted and directed graphs. It is based on the identification of pivot nodes that equally contribute to BC values of the other nodes of the network discovered via a cluster-based approach.In this paper, we focus on the performance and scalability analysis of the proposed algorithm in order to characterize its behavior with different sets of computing resources and to identify room for further improvements. To this end, we exploit a real dataset related to a transportation network. The results show that the proposed algorithm exhibits significantly lower execution times if compared with the Brandes's solution for computing exact BC values, especially when the number of available computing resources is limited. However, the speedup is not negligible even when the number of resources grows, where improvements are possible, as shown by our analysis.