Abstract
Developers spend significant time and effort in maintaining software. In a maintenance task, developers sometimes have to simultaneously modify multiple program entities (i.e., classes, methods, and fields). We refer to such complex changes as multi-entity edits. It is challenging for developers to apply multi-entity edits consistently and completely. Existing tools provide limited support for such edits, mainly because the co-changed entities usually contain diverse program contexts and experience different changes. This paper introduces CMSuggester, an automatic approach that suggests complementary changes for multi-entity edits. Given a multi-entity edit that adds a field and modifies one or more methods to access the field, CMSuggester suggests other methods to co-change for the new field access. CMSuggester is inspired by our previous empirical study, which reveals that the methods co-changed to access a new field usually commonly access the same set of fields declared in the same class. By extracting the fields accessed by the given changed method(s), CMSuggester identifies and recommends any unchanged method that also accesses those fields.
Our evaluation shows that CMSuggester recommends changes for 279 out of 408 suggestion tasks. With the recommended methods, CMSuggester achieves 73% F-score on average, while the widely used tool ROSE achieves 48% F-score. In most cases, as shown in our evaluation results, CMSuggester are useful for developers, since it recommend complete and correct multi-entity edits.
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Acknowledgment
We thank anonymous reviewers for their valuable comments on our earlier version of the paper. This work was supported by NSF Grant CCF-1565827, National Basic Research Program of China (973 Program) No. 2015CB352203, the National Nature Science Foundation of China No. 61572313, and the grant of Science and Technology Commission of Shanghai Municipality No. 15DZ1100305.
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Wang, Y., Meng, N., Zhong, H. (2018). CMSuggester: Method Change Suggestion to Complement Multi-entity Edits. In: Bu, L., Xiong, Y. (eds) Software Analysis, Testing, and Evolution. SATE 2018. Lecture Notes in Computer Science(), vol 11293. Springer, Cham. https://doi.org/10.1007/978-3-030-04272-1_9
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