2 research outputs found
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Όλ¬Έμμλ μμ κΈ°μ λ€μ μλ°ν μ μνκ³ κ·Έ κΈ°μ λ€μ΄ μ€μ C νλ‘ κ·Έλ¨ λΆμμμ μ±κ³΅μ μΌλ‘ μ μ©λ μ μμμ μ€νμ μΌλ‘ 보μΈλ€.As programs become larger and more complex, users of static analyzers
often encounter three usability issues. Firstly, static analyzers
often produce a large number of both true and false alarms that are
tedious to classify manually. Secondly, users cannot but wait long
time without any progress information during analysis. Lastly,
copy-right concerns over software sources hinder extensive uses of
static analyzers.
In this dissertation, we present our solutions to the three usability
issues. To reduce users' alarm-classification efforts, we propose a
sound method for clustering static analysis alarms. Our method
clusters alarms by discovering sound dependencies between them such
that if the dominant alarms of a cluster turns out to be false, all
the other alarms in the same cluster are guaranteed to be false. Once
clusters are found, users only need to investigate their dominant
alarms. Next, we present a progress indicator of static analyzers.
Our technique first combines a semantic-based pre-analysis and a
statistical method to approximate how a main analysis progresses in
terms of lattice height of the abstract domain. Then, we use this
information during the main analysis and estimate the analysis
current progress. Lastly, we present a static analysis of encrypted
programs to resolve users' copy-right concerns over software sources.
Users have purchased expensive commercial static analyzers or
outsource static analyses on their programs to analysis servers taking
the risk of loss of copy-right. Our method allows program owners to
encrypt and upload their programs to the static analysis service while
the service provider can still analyze the encrypted programs without
decrypting them.
We have implemented all the methods on top of a realistic static
analyzer for C programs and empirically proved that our methods
effectively improve the usability.Chapter 1 Overview 1
1.1 Problems 1
1.2 Solutions 3
1.3 Outline 4
Chapter 2 Preliminaries 6
2.1 Concepts 6
2.2 Static Analyses We Use 9
2.2.1 Interval Analysis 9
2.2.2 Octagon Analysis 12
2.2.3 Pointer Analysis 13
Chapter 3 Method 1. Sound Non-statistical Alarm Clustering 14
3.1 Introduction 14
3.1.1 Problem 14
3.1.2 OurSolution 15
3.1.3 Examples 15
3.1.4 Contributions 18
3.1.5 Outline 19
3.2 AlarmClusteringFramework 19
3.2.1 Static Analysis 19
3.2.2 AlarmClustering 19
3.3 Alarm-Clustering Algorithms 24
3.3.1 Algorithm 1: Finding Minimal Dominant Alarms 26
3.3.2 Algorithm 2: Non-Minimal but Efficient 30
3.4 Instances 32
3.4.1 Setting : Baseline Analyzer 34
3.4.2 Clustering using Interval Domain 34
3.4.3 Clustering using Octagon Domain 36
3.4.4 Clustering using Symbolic Execution 39
3.5 Experiments 41
Chapter 4 Method 2. A Progress Bar for Static Analyzers 47
4.1 Introduction 47
4.2 Overall Approach to Progress Estimation 48
4.2.1 Static Analysis 49
4.2.2 ProgressEstimation 49
4.3 Setting 52
4.4 Details on Our Progress Estimation 53
4.4.1 The Height Function 54
4.4.2 Pre-analysis via Partial Flow-Sensitivity 55
4.4.3 Precise Estimation of the Final Height 57
4.5 Experiments 59
4.5.1 Setting 60
4.5.2 Results 60
4.5.3 Discussion 62
4.6 Application to Relational Analyses 63
Chapter 5 Method 3. Static Analysis with Set-closure in Secrecy 65
5.1 Introduction 65
5.2 Background 67
5.2.1 Homomorphic Encryption 68
5.2.2 TheBGV-type crypto system 70
5.2.3 Security Model 71
5.3 A Basic Construction of a Pointer Analysis in Secrecy 71
5.3.1 A Brief Review of a Pointer Analysis 72
5.3.2 The Pointer Analysis in Secrecy 72
5.4 Improvement of the Pointer Analysis in Secrecy 76
5.4.1 Problems of the Basic Approach 76
5.4.2 Overview of Improvement 77
5.4.3 Level-by-levelAnalysis 77
5.4.4 Ciphertext Packing 80
5.4.5 Randomization of Ciphertexts 83
5.5 Experimental Result 83
5.6 Discussion 84
Chapter 6 Related Works 86
6.1 Sound Non-statistical Alarm Clustering 86
6.2 A Progress Bar for StaticAnalyzers 87
6.3 Static Analysis with Set-closure in Secrecy 88
Chapter 7 Conclusions 89
Chapter 8 Appendix 100
A Proofs of Theorems 100
B Progress Graph 107
C Algorithms for the Pointer Analysis in Secrecy 110
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