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ASE 2021
Sun 14 - Sat 20 November 2021 Australia
Tue 16 Nov 2021 21:20 - 21:40 at Koala - APIs Chair(s): Timo Kehrer

Recommender systems in software engineering provide developers with a wide range of valuable items to help them complete their tasks. Among others, API recommender systems have gained momentum in recent years as they became more successful at suggesting API calls or code snippets. While these systems have proven to be effective in terms of prediction accuracy, there has been less attention for what concerns such recommenders’ resilience against adversarial attempts. In fact, by crafting the recommenders’ learning material, e.g., data from large open-source software (OSS) repositories, hostile users may succeed in injecting malicious data, putting at risk the software clients adopting API recommender systems. In this paper, we present an empirical investigation of adversarial machine learning techniques and their possible influence on recommender systems. The evaluation performed on three state-of-the-art API recommender systems reveals a worrying outcome: all of them are not immune to malicious data. The obtained result triggers the need for effective countermeasures to protect recommender systems against hostile attacks disguised in training data.

Tue 16 Nov

Displayed time zone: Hobart change

21:00 - 22:00
APIs Research Papers at Koala
Chair(s): Timo Kehrer Humboldt University of Berlin
Finding Replacements for Missing APIs in Library Update
Research Papers
Kaifeng Huang Fudan University, Bihuan Chen Fudan University, China, Linghao Pan Fudan University, Shuai Wu Fudan University, Xin Peng Fudan University
Adversarial Attacks to API Recommender Systems: Time to Wake Up and Smell the Coffee?
Research Papers
Phuong T. Nguyen University of L’Aquila, Claudio Di Sipio University of L'Aquila, Juri Di Rocco University of L'Aquila, Massimiliano Di Penta University of Sannio, Italy, Davide Di Ruscio University of L'Aquila