2 research outputs found
SATDBailiff- Mining and Tracking Self-Admitted Technical Debt
Self-Admitted Technical Debt (SATD) is a metaphorical concept to describe the self-documented addition of technical debt to a software project in the form of source code comments. SATD can linger in projects and degrade source-code quality, but it can also be more visible than unintentionally added or undocumented technical debt. Understanding the implications of adding SATD to a software project is important because developers can benefit from a better understanding of the quality trade-offs they are making. However, empirical studies, analyzing the survivability and removal of SATD comments, are challenged by potential code changes or SATD comment updates that may interfere with properly tracking their appearance, existence, and removal. In this paper, we propose SATDBailiff, a tool that uses an existing state-of-the-art SATD detection tool, to identify SATD in method comments, then properly track their lifespan. SATDBailiff is given as input links to open source projects, and its output is a list of all identified SATDs, and for each detected SATD, SATDBailiff reports all its associated changes, including any updates to its text, all the way to reporting its removal. The goal of SATDBailiff is to aid researchers and practitioners in better tracking SATDs instances, and providing them with a reliable tool that can be easily extended. SATDBailiff was validated using a dataset of previously detected and manually validated SATD instances. SATDBailiff is publicly available as an open source, along with the manual analysis of SATD instances associated with its validation, on the project website
A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms
Abstract syntax tree (AST) mapping algorithms are widely used to analyze
changes in source code. Despite the foundational role of AST mapping
algorithms, little effort has been made to evaluate the accuracy of AST mapping
algorithms, i.e., the extent to which an algorihtm captures the evolution of
code. We observe that a program element often has only one best-mapped program
element. Based on this observation, we propose a hierarchical approach to
automatically compare the similarity of mapped statements and tokens by
different algorithms. By performing the comparison, we determine if each of the
compared algorithms generates inaccurate mappings for a statement or its
tokens. We invite 12 external experts to determine if three commonly used AST
mapping algorithms generate accurate mappings for a statement and its tokens
for 200 statements. Based on the experts' feedback,we observe that our approach
achieves a precision of 0.98--1.00 and a recall of 0.65--0.75. Furthermore, we
conduct a large-scale study with a dataset of ten Java projects, containing a
total of 263,165 file revisions. Our approach determines that GumTree, MTDiff
and IJM generate inaccurate mappings for 20%--29%, 25%--36% and 21%--30% of the
file revisions, respectively. Our experimental results show that state-of-art
AST mapping agorithms still need improvements