2019 IEEE/ACM 27th International Conference on Program Comprehension (ICPC)

Anthology ID:
Bib Export formats:

pdf bib
Comparing Bug Replication in Regular and Micro Code Clones
Judith F. Islam | Manishankar Mondal | Chanchal K. Roy | Kevin A. Schneider

Copying and pasting source code during software development is known as code cloning. Clone fragments with a minimum size of 5 LOC were usually considered in previous studies. In recent studies, clone fragments which are less than 5 LOC are referred as micro-clones. It has been established by the literature that code clones are closely related with software bugs as well as bug replication. None of the previous studies have been conducted on bug-replication of micro-clones. In this paper we investigate and compare bug-replication in between regular and micro-clones. For the purpose of our investigation, we analyze the evolutionary history of our subject systems and identify occurrences of similarity preserving co-changes (SPCOs) in both regular and micro-clones where they experienced bug-fixes. From our experiment on thousands of revisions of six diverse subject systems written in three different programming languages, C, C# and Java we find that the percentage of clone fragments that take part in bug-replication is often higher in micro-clones than in regular code clones. The percentage of bugs that get replicated in micro-clones is almost the same as the percentage in regular clones. Finally, both regular and micro-clones have similar tendencies of replicating severe bugs according to our experiment. Thus, micro-clones in a code-base should not be ignored. We should rather consider these equally important as of the regular clones when making clone management decisions.

pdf bib
Recommending Comprehensive Solutions for Programming Tasks by Mining Crowd Knowledge
Rodrigo F. G. Silva | Chanchal K. Roy | Mohammad Masudur Rahman | Kevin A. Schneider | Klérisson V. R. Paixão | Marcelo de Almeida Maia

Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face two major problems. First, the search is impaired due to a lexical gap between their query (task description) and the information associated with the solution. Second, the retrieved solution may not be comprehensive, i.e., the code segment might miss a succinct explanation. These problems make the developers browse dozens of documents in order to synthesize an appropriate solution. To address these two problems, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) and provides a comprehensive solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations. Our proposed approach expands the task description with relevant API classes from Stack Overflow Q & A threads and then mitigates the lexical gap problems. Furthermore, we perform natural language processing on the top quality answers and then return such programming solutions containing code examples and code explanations unlike earlier studies. We evaluate our approach using 97 programming queries, of which 50% was used for training and 50% was used for testing, and show that it outperforms six baselines including the state-of-art by a statistically significant margin. Furthermore, our evaluation with 29 developers using 24 tasks (queries) confirms the superiority of CROKAGE over the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation).