NDLI: Efficient elimination of false positives using static analysis

Content Provider	IEEE Xplore Digital Library
Author	Muske, T. Khedker, U.P.
Copyright Year	2015
Description	Author affiliation: Tata Res. Dev. & Design Centre, Pune, India (Muske, T.) \|\| Indian Inst. of Technol., Bombay, Mumbai, India (Khedker, U.P.)
Abstract	Bug detection using static analysis has been found useful in practice for ensuring software quality and reliability. However, it often requires sifting through a large number of warnings. This can be handled by generating an assertion corresponding to each warning and verifying the assertion using a model checker to classify the warning as an error or a false positive. Since model checking over larger code fragments is non-scalable and expensive, it is useful to model check a given assertion with a small calling context length. For this, the variables receiving values from outside the context are initialized with arbitrary values generated by non-deterministic choice functions. The calling context length is then gradually increased on a need basis to include callers higher up in the call chains. While this aids scalability by keeping the calling context as small as possible, it requires multiple calls to model checker for the same assertion, requiring a considerable amount of time. We present a static analysis to expedite false positive elimination. It is based on the following observation: When the variables involved in an assertion are allowed to take arbitrary values at the point of assertion, the assertion is most likely to be violated by some or the other combination of values. In such cases, usage of a model checker is redundant as it does not aid in resolution of the corresponding warning. Our data flow analysis identifies (an over-approximated set of) such variables using a novel lattice so that model checking of assertions involving such variables can be avoided. Our empirical evaluation demonstrates that, on an average, the proposed static analysis avoids 49.49% of the total model checking calls, and it reduces the false positives elimination time by 39.28%. However, this gain is achieved at the cost of missing 2.78% false positives which could have been eliminated otherwise.
Starting Page	270
Ending Page	280
File Size	142665
Page Count	11
File Format	PDF
e-ISBN	9781509004065
DOI	10.1109/ISSRE.2015.7381820
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Model checking Context Input variables Context modeling Scalability Reactive power Analytical models
Content Type	Text
Resource Type	Article

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

Efficient Elimination of False Positives Using Bounded Model Checking

Effective false positive filtering for evolving software

Reduction Techniques for Model Checking Markov Decision Processes

Conversion of fast inter-procedural static analysis to model checking

Battles with false positives in static analysis of JavaScript web applications in the wild

Effect of SVCs on transmission system voltage profile for different static load models

Safety Analysis of Trampoline OS Using Model Checking: An Experience Report

A Study on Information Granular-Driven Polynomial Neural Networks

Using model checking to analyze static properties of declarative models

Efficient elimination of false positives using static analysis

Similar Documents

Efficient Elimination of False Positives Using Bounded Model Checking

Effective false positive filtering for evolving software

Reduction Techniques for Model Checking Markov Decision Processes

Conversion of fast inter-procedural static analysis to model checking

Battles with false positives in static analysis of JavaScript web applications in the wild

Effect of SVCs on transmission system voltage profile for different static load models

Safety Analysis of Trampoline OS Using Model Checking: An Experience Report

A Study on Information Granular-Driven Polynomial Neural Networks

Using model checking to analyze static properties of declarative models

Efficient elimination of false positives using static analysis