Yi Li
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- ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings (3)
- ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2)
- ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2)
- ICSE '21: Proceedings of the 43rd International Conference on Software Engineering (2)
- ICSE '23: Proceedings of the 45th International Conference on Software Engineering (2)
- ICSE-Companion '24: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (2)
- ASE '23: Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering (1)
- ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (1)
- ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (1)
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- MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories (1)
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- research-article
Contextuality of Code Representation Learning
- Yi Li
Department of Informatics New Jersey Institute of Technology, New Jersey, USA
, - Shaohua Wang
Department of Informatics New Jersey Institute of Technology, New Jersey, USA
, - Tien N. Nguyen
Computer Science Department, The University of Texas at Dallas, Texas, USA
ASE '23: Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering•November 2023, pp 548-559• https://doi.org/10.1109/ASE56229.2023.00029Advanced machine learning models (ML) have been successfully leveraged in several software engineering (SE) applications. The existing SE techniques have used the embedding models ranging from static to contextualized ones to build the vectors for ...
- 0Citation
- 4
- Downloads
MetricsTotal Citations0Total Downloads4Last 12 Months4
- Yi Li
- short-paperOpen AccessPublished By ACMPublished By ACM
Neural Exception Handling Recommender
- Yi Li
New Jersey Institute of Technology, Newark, New Jersey, USA
, - Tien N. Nguyen
Computer Science, University of Texas at Dallas, Dallas, Texas, United States of America
, - Yuchen Cai
Computer Science, University of Texas at Dallas, Dallas, Texas, USA
, - Aashish Yadavally
Computer Science, University of Texas at Dallas, Dallas, United States of America
, - Abhishek Mishra
Computer Science, University of Texas at Dallas, Dallas, Texas, USA
, - Genesis Montejo
Computer Science, University of Texas at Dallas, Dallas, Texas, USA
ICSE-Companion '24: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings•April 2024, pp 316-317• https://doi.org/10.1145/3639478.3643082Practical code reuse often leads to the incorporation of code fragments from developer forums into applications. However, these fragments, being incomplete, frequently lack details on exception handling. Integrating exception handling into a codebase is ...
- 0Citation
- 113
- Downloads
MetricsTotal Citations0Total Downloads113Last 12 Months113Last 6 weeks20
- Yi Li
- short-paperOpen AccessPublished By ACMPublished By ACM
Poirot: Deep Learning for API Misuse Detection
- Yi Li
New Jersey Institute of Technology, Newark, New Jersey, USA
, - Tien N. Nguyen
University of Texas at Dallas, Dallas, United States of America
, - Shaohua Wang
Central University of Finance and Economics, Bejing, China
, - Aashish Yadavally
University of Texas at Dallas, Dallas, Texas, United States of America
ICSE-Companion '24: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings•April 2024, pp 302-303• https://doi.org/10.1145/3639478.3643080API misuses refer to incorrect usages that violate the usage constraints of API elements, potentially leading to issues such as runtime errors, exceptions, program crashes, and security vulnerabilities. Existing mining-based approaches for API misuse ...
- 0Citation
- 189
- Downloads
MetricsTotal Citations0Total Downloads189Last 12 Months189Last 6 weeks21
- Yi Li
- research-articleOpen AccessPublished By ACMPublished By ACM
DeMinify: Neural Variable Name Recovery and Type Inference
- Yi Li
New Jersey Institute of Technology, Newark, USA
, - Aashish Yadavally
University of Texas at Dallas, Richardson, USA
, - Jiaxing Zhang
New Jersey Institute of Technology, Newark, USA
, - Shaohua Wang
New Jersey Institute of Technology, Newark, USA
, - Tien N. Nguyen
University of Texas at Dallas, Richardson, USA
ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering•November 2023, pp 758-770• https://doi.org/10.1145/3611643.3616368To avoid the exposure of original source code, the variable names deployed in the wild are often replaced by short, meaningless names, thus making the code difficult to understand and be analyzed. We introduce DeMinify, a Deep-Learning (DL)-based ...
- 0Citation
- 464
- Downloads
MetricsTotal Citations0Total Downloads464Last 12 Months408Last 6 weeks36- 1
Supplementary Materialfse23main-p1643-p-video.mp4
- Yi Li
- research-articleOpen AccessPublished By ACMPublished By ACM
Commit-Level, Neural Vulnerability Detection and Assessment
- Yi Li
New Jersey Institute of Technology, Newark, USA
, - Aashish Yadavally
University of Texas at Dallas, Richardson, USA
, - Jiaxing Zhang
New Jersey Institute of Technology, Newark, USA
, - Shaohua Wang
New Jersey Institute of Technology, Newark, USA
, - Tien N. Nguyen
University of Texas at Dallas, Richardson, USA
ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering•November 2023, pp 1024-1036• https://doi.org/10.1145/3611643.3616346Software Vulnerabilities (SVs) are security flaws that are exploitable in cyber-attacks. Delay in the detection and assessment of SVs might cause serious consequences due to the unknown impacts on the attacked systems. The state-of-the-art approaches ...
- 3Citation
- 795
- Downloads
MetricsTotal Citations3Total Downloads795Last 12 Months640Last 6 weeks75- 1
Supplementary Materialfse23main-p1185-p-video.mp4
- Yi Li
- research-article
Does Data Sampling Improve Deep Learning-Based Vulnerability Detection? Yeas! and Nays!
- Xu Yang
University of Manitoba, Canada
, - Shaowei Wang
University of Manitoba, Canada
, - Yi Li
New Jersey Institute of Technology, USA
, - Shaohua Wang
New Jersey Institute of Technology, USA
ICSE '23: Proceedings of the 45th International Conference on Software Engineering•May 2023, pp 2287-2298• https://doi.org/10.1109/ICSE48619.2023.00192Recent progress in Deep Learning (DL) has sparked interest in using DL to detect software vulnerabilities automatically and it has been demonstrated promising results at detecting vulnerabilities. However, one prominent and practical issue for ...
- 3Citation
- 76
- Downloads
MetricsTotal Citations3Total Downloads76Last 12 Months47Last 6 weeks6
- Xu Yang
- research-article
DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection
- Wenbo Wang
Department of Informatics, New Jersey Institute of Technology, New Jersey, USA
, - Tien N. Nguyen
Computer Science Department, The University of Texas at Dallas, Texas, USA
, - Shaohua Wang
Department of Informatics, New Jersey Institute of Technology, New Jersey, USA
, - Yi Li
Department of Informatics, New Jersey Institute of Technology, New Jersey, USA
, - Jiyuan Zhang
Computer Science Department, University of Illinois Urbana-Champaign, Illinois, USA
, - Aashish Yadavally
Computer Science Department, The University of Texas at Dallas, Texas, USA
ICSE '23: Proceedings of the 45th International Conference on Software Engineering•May 2023, pp 2249-2261• https://doi.org/10.1109/ICSE48619.2023.00189The advances of machine learning (ML) including deep learning (DL) have enabled several approaches to implicitly learn vulnerable code patterns to automatically detect software vulnerabilities. A recent study showed that despite successes, the existing ...
- 6Citation
- 87
- Downloads
MetricsTotal Citations6Total Downloads87Last 12 Months50Last 6 weeks6
- Wenbo Wang
- research-articleOpen AccessPublished By ACMPublished By ACM
UTANGO: untangling commits with context-aware, graph-based, code change clustering learning model
- Yi Li
New Jersey Institute of Technology, USA
, - Shaohua Wang
New Jersey Institute of Technology, USA
, - Tien N. Nguyen
University of Texas at Dallas, USA
ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering•November 2022, pp 221-232• https://doi.org/10.1145/3540250.3549171During software evolution, developers make several changes and commit them into the repositories. Unfortunately, many of them tangle different purposes, both hampering program comprehension and reducing separation of concerns. Automated approaches ...
- 4Citation
- 645
- Downloads
MetricsTotal Citations4Total Downloads645Last 12 Months299Last 6 weeks29
- Yi Li
- research-articleOpen AccessPublished By ACMPublished By ACM
Fault localization to detect co-change fixing locations
- Yi Li
New Jersey Institute of Technology, USA
, - Shaohua Wang
New Jersey Institute of Technology, USA
, - Tien N. Nguyen
University of Texas at Dallas, USA
ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering•November 2022, pp 659-671• https://doi.org/10.1145/3540250.3549137Fault Localization (FL) is a precursor step to most Automated Program Repair (APR) approaches, which fix the faulty statements identified by the FL tools. We present FixLocator, a Deep Learning (DL)-based fault localization approach supporting the ...
- 5Citation
- 794
- Downloads
MetricsTotal Citations5Total Downloads794Last 12 Months320Last 6 weeks33
- Yi Li
- research-articleOpen AccessPublished By ACMPublished By ACM
DEAR: a novel deep learning-based approach for automated program repair
- Yi Li
New Jersey Inst. of Technology
, - Shaohua Wang
New Jersey Inst. of Technology
, - Tien N. Nguyen
University of Texas at Dallas
ICSE '22: Proceedings of the 44th International Conference on Software Engineering•May 2022, pp 511-523• https://doi.org/10.1145/3510003.3510177The existing deep learning (DL)-based automated program repair (APR) models are limited in fixing general software defects. We present DEAR, a DL-based approach that supports fixing for the general bugs that require dependent changes at once to one or ...
- 41Citation
- 1,591
- Downloads
MetricsTotal Citations41Total Downloads1,591Last 12 Months569Last 6 weeks71
- Yi Li
- research-article
Fault Localization with Code Coverage Representation Learning
- Yi Li
Department of Informatics, New Jersey Institute of Technology, New Jersey, USA
, - Shaohua Wang
Department of Informatics, New Jersey Institute of Technology, New Jersey, USA
, - Tien N. Nguyen
Computer Science Department, The University of Texas at Dallas, Texas, USA
ICSE '21: Proceedings of the 43rd International Conference on Software Engineering•May 2021, pp 661-673• https://doi.org/10.1109/ICSE43902.2021.00067In this paper, we propose DEEPRL4FL, a deep learning fault localization (FL) approach that locates the buggy code at the statement and method levels by treating FL as an image pattern recognition problem. DEEPRL4FL does so via novel code coverage ...
- 25Citation
- 232
- Downloads
MetricsTotal Citations25Total Downloads232Last 12 Months58Last 6 weeks7
- Yi Li
- research-article
A Context-based Automated Approach for Method Name Consistency Checking and Suggestion
- Yi Li
Department of Informatics, New Jersey Institute of Technology, New Jersey, USA
, - Shaohua Wang
Department of Informatics, New Jersey Institute of Technology, New Jersey, USA
, - Tien N. Nguyen
Computer Science Department, The University of Texas at Dallas, Texas, USA
ICSE '21: Proceedings of the 43rd International Conference on Software Engineering•May 2021, pp 574-586• https://doi.org/10.1109/ICSE43902.2021.00060Misleading method names in software projects can confuse developers, which may lead to software defects and affect code understandability. In this paper, we present DeepName, a context-based, deep learning approach to detect method name inconsistencies ...
- 12Citation
- 60
- Downloads
MetricsTotal Citations12Total Downloads60Last 12 Months8
- Yi Li
- research-articlePublic AccessPublished By ACMPublished By ACM
Vulnerability detection with fine-grained interpretations
- Yi Li
New Jersey Institute of Technology, USA
, - Shaohua Wang
New Jersey Institute of Technology, USA
, - Tien N. Nguyen
University of Texas at Dallas, USA
ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering•August 2021, pp 292-303• https://doi.org/10.1145/3468264.3468597Despite the successes of machine learning (ML) and deep learning (DL)-based vulnerability detectors (VD), they are limited to providing only the decision on whether a given code is vulnerable or not, without details on what part of the code is relevant ...
- 116Citation
- 3,248
- Downloads
MetricsTotal Citations116Total Downloads3,248Last 12 Months1,517Last 6 weeks195
- Yi Li
- posterPublished By ACMPublished By ACM
An empirical study on the characteristics of question-answering process on developer forums
- Yi Li
New Jersey Inst. of Technology
, - Shaohua Wang
New Jersey Inst. of Technology
, - Tien N. Nguyen
University of Texas at Dallas
ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings•June 2020, pp 318-319• https://doi.org/10.1145/3377812.3390897Developer forums are one of the most popular and useful Q&A websites on API usages. The analysis of API forums can be a critical step towards automated question and answer approaches. In this poster, we empirically study three API forums: Twitter, eBay, ...
- 1Citation
- 81
- Downloads
MetricsTotal Citations1Total Downloads81Last 12 Months8
- Yi Li
- posterPublished By ACMPublished By ACM
Improving automated program repair using two-layer tree-based neural networks
- Yi Li
New Jersey Inst. of Technology
, - Shaohua Wang
New Jersey Inst. of Technology
, - Tien N. Nguyen
University of Texas at Dallas
ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings•June 2020, pp 316-317• https://doi.org/10.1145/3377812.3390896We present DLFix, a two-layer tree-based model learning bug-fixing code changes and their surrounding code context to improve Automated Program Repair (APR). The first layer learns the surrounding code context of a fix and uses it as weights for the ...
- 2Citation
- 170
- Downloads
MetricsTotal Citations2Total Downloads170Last 12 Months6Last 6 weeks1
- Yi Li
- short-paperPublished By ACMPublished By ACM
Improving bug detection and fixing via code representation learning
- Yi Li
New Jersey Institute of Technology
ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings•June 2020, pp 137-139• https://doi.org/10.1145/3377812.3382172The software quality and reliability have been proved to be important during the program development. There are many existing studies trying to help improve it on bug detection and automated program repair processes. However, each of them has its own ...
- 2Citation
- 266
- Downloads
MetricsTotal Citations2Total Downloads266Last 12 Months24
- Yi Li
- research-articlePublished By ACMPublished By ACM
DLFix: context-based code transformation learning for automated program repair
- Yi Li
New Jersey Inst. of Technology
, - Shaohua Wang
New Jersey Inst. of Technology
, - Tien N. Nguyen
University of Texas at Dallas
ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering•June 2020, pp 602-614• https://doi.org/10.1145/3377811.3380345Automated Program Repair (APR) is very useful in helping developers in the process of software development and maintenance. Despite recent advances in deep learning (DL), the DL-based APR approaches still have limitations in learning bug-fixing code ...
- 142Citation
- 2,496
- Downloads
MetricsTotal Citations142Total Downloads2,496Last 12 Months402Last 6 weeks42
- Yi Li
- short-paperPublished By ACMPublished By ACM
A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries
- Jiahao Fan
SPACE Lab, Informatics, New Jersey Institute of Technology
, - Yi Li
SPACE Lab, Informatics, New Jersey Institute of Technology
, - Shaohua Wang
SPACE Lab, Informatics, New Jersey Institute of Technology
, - Tien N. Nguyen
CS Department, The University of Texas at Dallas
MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories•June 2020, pp 508-512• https://doi.org/10.1145/3379597.3387501We collected a large C/C++ code vulnerability dataset from open-source Github projects, namely Big-Vul. We crawled the public Common Vulnerabilities and Exposures (CVE) database and CVE-related source code repositories. Specifically, we collected the ...
- 184Citation
- 3,642
- Downloads
MetricsTotal Citations184Total Downloads3,642Last 12 Months1,382Last 6 weeks111
- Jiahao Fan
- research-articleOpen AccessPublished By ACMPublished By ACM
Improving bug detection via context-based code representation learning and attention-based neural networks
- Yi Li
New Jersey Institute of Technology, USA
, - Shaohua Wang
New Jersey Institute of Technology, USA
, - Tien N. Nguyen
University of Texas at Dallas, USA
, - Son Van Nguyen
University of Texas at Dallas, USA
Proceedings of the ACM on Programming Languages, Volume 3, Issue OOPSLA•October 2019, Article No.: 162, pp 1-30 • https://doi.org/10.1145/3360588Bug detection has been shown to be an effective way to help developers in detecting bugs early, thus, saving much effort and time in software development process. Recently, deep learning-based bug detection approaches have gained successes over the ...
- 108Citation
- 4,046
- Downloads
MetricsTotal Citations108Total Downloads4,046Last 12 Months747Last 6 weeks74- 1
Supplementary Materiala162-li.webm
- Yi Li
Author Profile Pages
- Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples: - History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
- Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.
Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
- Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
- Future Direction:
The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.
A direct search interface for Author Profiles will be built.
An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.
It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.
Bibliometrics
The ACM DL is a comprehensive repository of publications from the entire field of computing.
It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.
- Average citations per article = The total Citation Count divided by the total Publication Count.
- Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
- Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
- Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
- Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
- Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
- Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
- Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
- Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)
ACM Author-Izer Service
Summary Description
ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.
Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.
ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.
To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.
How ACM Author-Izer Works
Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.
The Service can be applied to all the articles you have ever published with ACM.
Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.
For authors who do not have a free ACM Web Account:
- Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.
For authors who have an ACM web account, but have not edited their ACM Author Profile page:
- Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.
For authors who have an account and have already edited their Profile Page:
- Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"
ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.
Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.
FAQ
- Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
- Q. What articles are eligible for ACM Author-Izer?
- A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.
- Q. Are there any restrictions on authors to use this service?
- A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.
- Q. What are the requirements to use this service?
- A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.
- Q. What is an ACM Author Profile Page?
- A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.
- Q. How do I find my Author Profile page and take ownership?
- A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours
- Q. Why does my photo not appear?
- A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters
- Q. What if I cannot find the Add Personal Information function on my author page?
- A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.
- Q. What happens if an author changes the location of his bibliography or moves to a new institution?
- A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.
- Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
- A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.
However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.
- Q. What happens if the author’s bibliography lives on a page with several aliases?
- A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.
- Q. Why should authors use ACM Author-Izer?
- A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.
- Q. Does ACM Author-Izer provide benefits for authors?
- A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.
Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.
- Q. How does ACM Author-Izer benefit the computing community?
- A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.
In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.
- Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
- A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.
- Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
- A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner