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Parseweb: a programmer assistant for reusing open source code on the web

Authors:

Suresh Thummalapenta,

Tao XieAuthors Info & Claims

ASE '07: Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering

Pages 204 - 213

https://doi.org/10.1145/1321631.1321663

Published: 05 November 2007 Publication History

Abstract

Programmers commonly reuse existing frameworks or libraries to reduce software development efforts. One common problem in reusing the existing frameworks or libraries is that the programmers know what type of object that they need, but do not know how to get that object with a specific method sequence. To help programmers to address this issue, we have developed an approach that takes queries of the form "Source object type → Destination object type" as input, and suggests relevant method-invocation sequences that can serve as solutions that yield the destination object from the source object given in the query. Our approach interacts with a code search engine (CSE) to gather relevant code samples and performs static analysis over the gathered samples to extract required sequences. As code samples are collected on demand through CSE, our approach is not limited to queries of any specific set of frameworks or libraries. We have implemented our approach with a tool called PARSEWeb, and conducted four different evaluations to show that our approach is effective in addressing programmer's queries. We also show that PARSEWeb performs better than existing related tools: Prospector and Strathcona

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Cited By

Liu KWu JSun QYang HWan R(2024)Harnessing Test-Oriented Knowledge Graphs for Enhanced Test Function RecommendationElectronics10.3390/electronics1308154713:8(1547)Online publication date: 18-Apr-2024
https://doi.org/10.3390/electronics13081547
Jiang SCoblenz M(2024)An Analysis of the Costs and Benefits of Autocomplete in IDEsProceedings of the ACM on Software Engineering10.1145/36607651:FSE(1284-1306)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660765
Huang QLiao DXing ZYuan ZLu QXu XLu J(2024)SE Factual Knowledge in Frozen Giant Code Model: A Study on FQN and Its RetrievalIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.343688336:12(9220-9234)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3436883
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Index Terms

Parseweb: a programmer assistant for reusing open source code on the web
1. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments
      1. Integrated and visual development environments
    2. General programming languages
      1. Language types
        Object oriented languages

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Published In

cover image ACM Conferences

ASE '07: Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering

November 2007

590 pages

ISBN:9781595938824

DOI:10.1145/1321631

General Chair:
Kurt Stirewalt
Michigan State University, USA
,
Program Chairs:
Alexander Egyed
Teknowledge Corporation, USA
,
Bernd Fischer
University of Southampton, UK

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 05 November 2007

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ASE07

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ASE07: International Conference on Automated Software Engineering 2007

November 5 - 9, 2007

Georgia, Atlanta, USA

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Liu KWu JSun QYang HWan R(2024)Harnessing Test-Oriented Knowledge Graphs for Enhanced Test Function RecommendationElectronics10.3390/electronics1308154713:8(1547)Online publication date: 18-Apr-2024
https://doi.org/10.3390/electronics13081547
Jiang SCoblenz M(2024)An Analysis of the Costs and Benefits of Autocomplete in IDEsProceedings of the ACM on Software Engineering10.1145/36607651:FSE(1284-1306)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660765
Huang QLiao DXing ZYuan ZLu QXu XLu J(2024)SE Factual Knowledge in Frozen Giant Code Model: A Study on FQN and Its RetrievalIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.343688336:12(9220-9234)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3436883
CHENG YHUANG GWU YZHAO ZHE ZLU J(2024)Ask Me Any Type: Type Inference Plugin for Partial Code on the Web and in the Integrated Development EnvironmentWuhan University Journal of Natural Sciences10.1051/wujns/202429434929:4(349-356)Online publication date: 4-Sep-2024
https://doi.org/10.1051/wujns/2024294349
Gupta ASingh VMahajan SMalhotra YMishra SKaur KJindal N(2024)Web Platform as Path-Guide for Professional Students: A One-Stop SolutionJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-01143-7Online publication date: 9-Sep-2024
https://doi.org/10.1007/s40031-024-01143-7
Bibi NRana TMaqbool AAfzal FAkgül ADe la Sen M(2023)An Intelligent Platform for Software Component Mining and RetrievalSensors10.3390/s2301052523:1(525)Online publication date: 3-Jan-2023
https://doi.org/10.3390/s23010525
Huang QYuan ZXing ZPeng XXu XLu Q(2023)FQN Inference in Partial Code by Prompt-tuned Language Model of CodeACM Transactions on Software Engineering and Methodology10.1145/361717433:2(1-32)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3617174
Wang CHu JGao CJin YXie THuang HLei ZDeng YChandra SBlincoe KTonella P(2023)How Practitioners Expect Code Completion?Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616280(1294-1306)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616280
Kim KGhatpande SKim DZhou XLiu KBissyandé TKlein JLe Traon Y(2023)Big Code Search: A BibliographyACM Computing Surveys10.1145/360490556:1(1-49)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3604905
Zhong HWang X(2023)An empirical study on API usages from code search engine and local libraryEmpirical Software Engineering10.1007/s10664-023-10304-z28:3Online publication date: 13-Apr-2023
https://dl.acm.org/doi/10.1007/s10664-023-10304-z
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