Could We Infer Unordered API Usage Patterns Only Using the Library Source Code?

Saied, Mohamed Aymen; Abdeen, Hani; Benomar, Omar; Sahraoui, Houari

doi:10.1109/icpc.2015.16

Cited by 20 publications

(14 citation statements)

References 31 publications

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“…The NCBUP-miner (Non Client-based Usage Patterns) [35] is a technique that identifies unordered API usage patterns from the API source code, based on both structural (methods that modify the same object) and semantic (methods that have the same vocabulary) relations. The same authors also propose MLUP [34], which is based on vector representation and clustering, but in this case client code is also considered.…”

Section: A Api Usage Pattern Recommendationmentioning

confidence: 99%

FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns

Nguyen

Rocco

Ruscio

et al. 2019

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

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Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run-time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborativefiltering recommender system. We present a new tool, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the stateof-the-art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of contextaware collaborative-filtering recommender systems to provide API usage patterns.

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Section: A Api Usage Pattern Recommendationmentioning

confidence: 99%

FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns

Nguyen

Rocco

Ruscio

et al. 2019

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

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“…In VSM's space, each FR is represented as a vector (call). The semantic similarity (semanticSim) between two FRs is defined in Equation 1using cosine similarity as it is a well-known in our subject [34,35]. For two given FRs, this metric is used to determine how much relevant semantic information is shared among their corresponding token vectors:…”

Section: Computing Semantic Similaritymentioning

confidence: 99%

Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering

et al. 2018

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Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software services) and non-functional (quality attributes). The first step of every software development project is SR elicitation. This step is a challenge task for developers as they need to understand and analyze SRs manually. For example, the collected functional SRs need to be categorized into different clusters to break-down the project into a set of sub-projects with related SRs and devote each sub-project to a separate development team. However, functional SRs clustering has never been considered in the literature. Therefore, in this paper, we propose an approach to automatically cluster functional requirements based on semantic measure. An empirical evaluation is conducted using four open-access software projects to evaluate our proposal. The experimental results demonstrate that the proposed approach identifies semantic clusters according to well-known used measures in the subject.

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“…Current CSEs nearly wholly leverage text-oriented information-retrieval(IR) techniques that disregards inherent structure of source code. To address this problem, researchers have presented numerous approaches [9,14,19,15,17] to improve CSEs, further more, new recommendation engines are proposed. These are generally designed to work in the form of integrated development environment (IDE) plug-in to interact with programmers.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Ranking API Usage Pattern in Large Source Code Repository: A LFM Based Approach

Zhao¹,

Liu²

2017

Lecture Notes in Computer Science

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Code examples are key resources for helping programmers to learn correct Application Programming Interface (API) usages efficiently. However, most framework and library APIs fail in providing sufficient and adequate code examples in corresponding official documentations. Thus, it takes great programmers' efforts to browse and extract API usage examples from websites. To reduce such effort, this paper proposes a graph-based pattern-oriented mining approach, LFM-OUPD (Local fitness measure for detecting overlapping usage patterns) for API usage facility, that recommends proper API code examples from data analytics. API method queries are accepted from programmers and corresponding code files are collected from related API dataset. The detailed structural links among API method elements in conceptual source codes are captured and generate a code graph structure. Lancichinetti et al. proposed an overlapping community detecting algorithm (Local fitness measure, LFM), based on the local optimization of a fitness function. In LFM-OUPD, a mining algorithm based on LFM is presented to explore the division of method sequences in the directed source code element graph and detect candidates of different API usage patterns. Then a ranking approach is applied to obtain appropriate API usage pattern and code example candidates. A case study on Google Guava is conducted to evaluate the effectiveness of this approach.

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Could We Infer Unordered API Usage Patterns Only Using the Library Source Code?

Cited by 20 publications

References 31 publications

FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns

FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns

Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering

Detecting and Ranking API Usage Pattern in Large Source Code Repository: A LFM Based Approach

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