NDLI: Estimating uncaught exceptions in Standard ML programs from type-based equations

Content Provider	IEEE Xplore Digital Library
Author	Kwangkeun Yi Sukyoung Ryu Ki-Hyun Pyun
Copyright Year	1996
Description	Author affiliation: Dept. of Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Seoul, South Korea (Kwangkeun Yi; Sukyoung Ryu; Ki-Hyun Pyun)
Abstract	We present a static analysis that detects potential runtime exceptions that are raised and never handled inside Standard ML (SML) programs. Contrary to our earlier method (Yi, 1994) based on abstract interpretation, where the input program's control flow is simultaneously computed while our exception analysis progresses, we separate the two phases in a manner similar to conventional data flow analysis. Before the exception analysis begins, we first estimate the input program's control flow from the type information from SML/NJ compiler. Based on this call-graph structure, exception flow is specified as a set of equations, whose solution is computed using an iterative least fixpoint method. A prototype of this analysis is applied to two realistic SML programs (ML-LEX and OR-SML core) and is 3 or 40 times faster than the earlier method and saves memory by 35 or 65 percent.
Starting Page	455
Ending Page	460
File Size	533027
Page Count	6
File Format	PDF
ISBN	0818675799
ISSN	07303157
DOI	10.1109/CMPSAC.1996.544613
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-08-21
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Equations Safety Computer science Runtime Data analysis Programming profession Data flow computing Information analysis Program processors
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A cost-effective estimation of uncaught exceptions in standard ml programs (2000).

A cost-effective estimation of uncaught exceptions in standard ml programs (2002).

A practical method for estimating uncaught exceptions in sml programs (1996).

Compile-time Detection of Uncaught Exceptions in Standard ML Programs (1994)

Towards a Cost-effective Estimation of Uncaught Exceptions in SML Programs (1997)

Type-Based Analysis of Uncaught Exceptions (1998)

Type-based analysis of uncaught exceptions (1998).

Type-Based Analysis of Uncaught Exceptions (1999)

Type-Based Analysis of Uncaught Exceptions (1999)

Estimating uncaught exceptions in Standard ML programs from type-based equations

Similar Documents

A cost-effective estimation of uncaught exceptions in standard ml programs (2000).

A cost-effective estimation of uncaught exceptions in standard ml programs (2002).

A practical method for estimating uncaught exceptions in sml programs (1996).

Compile-time Detection of Uncaught Exceptions in Standard ML Programs (1994)

Towards a Cost-effective Estimation of Uncaught Exceptions in SML Programs (1997)

Type-Based Analysis of Uncaught Exceptions (1998)

Type-based analysis of uncaught exceptions (1998).

Type-Based Analysis of Uncaught Exceptions (1999)

Type-Based Analysis of Uncaught Exceptions (1999)

Estimating uncaught exceptions in Standard ML programs from type-based equations