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Data mining library reuse patterns using generalized association rules

Amir Michail

Open publisher page 152 citations

Abstract

In this paper, we show how data mining can be used to discover library reuse patterns in existing applications. Specifically, we consider the problem of discovering library classes and member functions that are typically reused in combination by application classes. This paper improves upon our earlier research using “association rules” [8] by taking into account the inheritance hierarchy using “generalized association rules”. This turns out to be a non-trivial but worthwhile endeavor.

About this research paper

What this paper is about

In this paper, we show how data mining can be used to discover library reuse patterns in existing applications. Specifically, we consider the problem of discovering library classes and member functions that are typically reused in combination by application classes. This paper improves upon our earlier research using “association rules” [8] by taking into account the inheritance hierarchy using “generalized association rules”. This turns out to be a non-trivial but worthwhile endeavor.

Why it matters

OpenAlex reports 152 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Available abstract

In this paper, we show how data mining can be used to discover library reuse patterns in existing applications. Specifically, we consider the problem of discovering library classes and member functions that are typically reused in combination by application classes. This paper improves upon our earlier research using “association rules” [8] by taking into account the inheritance hierarchy using “generalized association rules”. This turns out to be a non-trivial but worthwhile endeavor.

Key concepts: Association rule learning, Computer science, Reuse, Association (psychology), Data mining, Information retrieval, Data science, Engineering

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