Robert Heumüller: Leveraging project-specificity to find suitable specifications: student research abstract. In: ACM, (Hrsg.): Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018, ISBN: 978-1-4503-5191-1.

Abstract

Automated inference of API specifications is crucial for scaling various automated software engineering tasks such as bug-detection or bug-fixing. Prior research has therefore focused on techniques to improve the quality, diversity and availability of specifications. Consequently, efficient ways of retrieving the appropriate specifications for a particular task from a specification database will be necessary in future. In our research, we analyse projects using information retrieval techniques (tf-idf), to determine which specifications are characteristic for a project. Our hypothesis is that this knowledge can be exploited to significantly optimize the search process, by focussing on projects which are similar in terms of their characteristic API usages. Initial results indicate that a project-specificity based selection of specifications from different sources can produce candidate sets featuring a larger variety of interesting specifications than support-based, project-agnostic heuristics.

BibTeX (Download)

@inproceedings{Heum\"{u}ller2018,
title = {Leveraging project-specificity to find suitable specifications: student research abstract},
author = {Robert Heum\"{u}ller},
editor = {ACM},
doi = {10.1145/3167132.3167455},
isbn = {978-1-4503-5191-1},
year  = {2018},
date = {2018-04-09},
booktitle = {Proceedings of the 33rd Annual ACM Symposium on Applied Computing},
abstract = {Automated inference of API specifications is crucial for scaling various automated software engineering tasks such as bug-detection or bug-fixing. Prior research has therefore focused on techniques to improve the quality, diversity and availability of specifications. Consequently, efficient ways of retrieving the appropriate specifications for a particular task from a specification database will be necessary in future. In our research, we analyse projects using information retrieval techniques (tf-idf), to determine which specifications are characteristic for a project. Our hypothesis is that this knowledge can be exploited to significantly optimize the search process, by focussing on projects which are similar in terms of their characteristic API usages. Initial results indicate that a project-specificity based selection of specifications from different sources can produce candidate sets featuring a larger variety of interesting specifications than support-based, project-agnostic heuristics.},
keywords = {api usage pattern, information retrieval, source code mining},
pubstate = {published},
tppubtype = {inproceedings}
}