logseq/pages/hls__Nguyen et al. - Adversarial Attacks to API Recommender Systems Ti.md

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• To achieve a good trade-off between the coverage of existing studies on AML in RSSE and efficiency, we defined the search strategy by answering the following four W-questions [62](“W” stands for Which?, Where?, What?, and When?).• Which? Both automatic and manual searches were performed to look for relevant papers from conferences and journals.• Where? We conducted a literature analysis on premier venues in software engineering. In particular, there are nine conferences as follows: ICSE, ESEC/FSE, ASE, ICSME, ICST, ISSTA, ESEM, MSR, and SANER. Meanwhile, the following five journals were considered: TSE, TOSEM, EMSE, JSS, and IST.5 The selection of conferences and journals was performed so to include mainstream venues, as well as specialized ones for which RSSE are relevant. The automatic search was done on the SCOPUS database.6 We fetched all the papers published by a given edition (year) of a given venue (journal/conference) using the advanced search and export features.• What? For each paper collected, its title and abstract were extracted using a set of predefined keywords. To cover more possible results, we used regular expressions for searching, e.g., depending on the terms we may use case sensitive queries.• When? Since Adversarial Machine Learning is a recent research topic, we limit the search to the most five recent years, i.e., from 2016 to 2020. ls-type:: annotation hl-page:: 4 id:: 631a1a3a-2e2b-4edb-b6bd-80ef23b673d8