Early Detection of Flawed Structural Dependencies During Software Evolution

During software evolution, complex structural dependencies between source files pose a great challenge on maintenance activities. Some of these dependencies propagate defects among files, incurring frequent bugs or changes, and consuming significant maintenance costs. They can be referred to as flawed structural dependencies. In this paper, we proposed a method to identify these potential problematic dependencies at an early stage during software evolution, by combing structural and semantic dependencies, so that developers can save maintenance costs by fixing these issues in time. Our method works as follows: First, we extract structural dependencies from the source code syntax and semantic dependencies from the source code lexicon. Second, we collect suspect file pairs by calculating the difference between structural and semantic dependencies. Next, we exhaustively examine each source file in the system and locate the interaction of its impacted subordinated files and suspect file pairs (SFP) as suspect dependencies. Finally, we gather all the suspect dependencies as flawed structural dependencies candidates. We evaluate our method using 838 releases of 15 open source projects, including 33353 bug reports and 86690 revision commits. The detection result shows that our identified dependencies use 14% of all the files to capture almost 70% of top 10% bug-prone files or change-prone files with enough high precision: 92%. Moreover, our identified dependencies also incur 957% of bug frequencies and 1050% of change frequencies than average in future versions. In summary, our method can effectively and efficiently detect flawed structural dependencies in time during software evolution.