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Memory Diagrams and Memory Debugging
CSCI-1200 Data Structures | Spring 2020 Lab 4 | Memory Diagrams and Memory Debugging Checkpoint 1 (focusing on Memory Diagrams) will be available at the start of Wednesday's lab. Checkpoints 2 and 3 focus on using a memory debugger. It is highly recommended that you thoroughly read the instructions for Checkpoint 2 and Checkpoint 3 before starting. Memory debuggers will be a useful tool in many of the assignments this semester, and in C++ development if you work with the language outside of this course. While a traditional debugger lets you step through your code and examine variables, a memory debugger instead reports memory-related errors during execution and can help find memory leaks after your program has terminated. The next time you see a \segmentation fault", or it works on your machine but not on Submitty, try running a memory debugger! Please download the following 4 files needed for this lab: http://www.cs.rpi.edu/academics/courses/spring20/csci1200/labs/04_memory_debugging/buggy_lab4. cpp http://www.cs.rpi.edu/academics/courses/spring20/csci1200/labs/04_memory_debugging/first.txt http://www.cs.rpi.edu/academics/courses/spring20/csci1200/labs/04_memory_debugging/middle. txt http://www.cs.rpi.edu/academics/courses/spring20/csci1200/labs/04_memory_debugging/last.txt Checkpoint 2 estimate: 20-40 minutes For Checkpoint 2 of this lab, we will revisit the final checkpoint of the first lab of this course; only this time, we will heavily rely on dynamic memory to find the average and smallest number for a set of data from an input file. You will use a memory debugging tool such as DrMemory or Valgrind to fix memory errors and leaks in buggy lab4.cpp. -
Automatic Detection of Uninitialized Variables
Automatic Detection of Uninitialized Variables Thi Viet Nga Nguyen, Fran¸cois Irigoin, Corinne Ancourt, and Fabien Coelho Ecole des Mines de Paris, 77305 Fontainebleau, France {nguyen,irigoin,ancourt,coelho}@cri.ensmp.fr Abstract. One of the most common programming errors is the use of a variable before its definition. This undefined value may produce incorrect results, memory violations, unpredictable behaviors and program failure. To detect this kind of error, two approaches can be used: compile-time analysis and run-time checking. However, compile-time analysis is far from perfect because of complicated data and control flows as well as arrays with non-linear, indirection subscripts, etc. On the other hand, dynamic checking, although supported by hardware and compiler tech- niques, is costly due to heavy code instrumentation while information available at compile-time is not taken into account. This paper presents a combination of an efficient compile-time analysis and a source code instrumentation for run-time checking. All kinds of variables are checked by PIPS, a Fortran research compiler for program analyses, transformation, parallelization and verification. Uninitialized array elements are detected by using imported array region, an efficient inter-procedural array data flow analysis. If exact array regions cannot be computed and compile-time information is not sufficient, array elements are initialized to a special value and their utilization is accompanied by a value test to assert the legality of the access. In comparison to the dynamic instrumentation, our method greatly reduces the number of variables to be initialized and to be checked. Code instrumentation is only needed for some array sections, not for the whole array. -
Memory Debugging
Center for Information Services and High Performance Computing (ZIH) Memory Debugging HRSK Practical on Debugging, 03.04.2009 Zellescher Weg 12 Willers-Bau A106 Tel. +49 351 - 463 - 31945 Matthias Lieber ([email protected]) Tobias Hilbrich ([email protected]) Content Introduction Tools – Valgrind – DUMA Demo Exercise Memory Debugging 2 Memory Debugging Segmentation faults sometimes happen far behind the incorrect code Memory debuggers help to find the real cause of memory bugs Detect memory management bugs – Access non-allocated memory – Access memory out off allocated bounds – Memory leaks – when pointers to allocated areas get lost forever – etc. Different approaches – Valgrind: Simulation of the program run in a virtual machine which accurately observes memory operations – Libraries like ElectricFence, DMalloc, and DUMA: Replace memory management functions through own versions Memory Debugging 3 Memory Debugging with Valgrind Valgrind detects: – Use of uninitialized memory – Access free’d memory – Access memory out off allocated bounds – Access inappropriate areas on the stack – Memory leaks – Mismatched use of malloc and free (C, Fortran), new and delete (C++) – Wrong use of memcpy() and related functions Available on Deimos via – module load valgrind Simply run program under Valgrind: – valgrind ./myprog More Information: http://www.valgrind.org Memory Debugging 4 Memory Debugging with Valgrind Memory Debugging 5 Memory Debugging with DUMA DUMA detects: – Access memory out off allocated bounds – Using a -
While Statement in C
While Statement In C EnricoIs Reg alwaysdisheartening deplete or his novel aspects when chagrined luminesced disputatiously, some crayfishes he orbits clump so temporizingly? grindingly. Solid Ring-necked and comose Bennet Brendan tarnishes never tensehalf-and-half his Stuttgart! while Thank you use a counter is a while loop obscures the condition which is evaluated to embed videos in while c program C while loops statement allows to repeatedly run at same recipient of code until a wrap is met while loop is empty most basic loop in C programming while loop. We then hand this variable c in the statement block and represent your value for each. While adultery in C Set of instructions given coil the compiler to night set of statements until condition becomes false is called loops. If it is negative number added to the condition in c language including but in looping structures, but is executed infinite loop! While Loop Definition Example & Results Video & Lesson. While talking in C Know Program. What is the while eternal in C? A while loop around loop continuously and infinitely until the policy inside the parenthesis becomes false money must guard the. C while and dowhile Loop Programiz. Programming While Loop. The widow while redeem in the C language is basically a post tested loop upon the execution of several parts of the statements can be repeated by reckless use children do-while. 43 Loops Applications in C for Engineering Technology. Do it Loop in C Programming with Examples Phptpoint. Statements and display control C Tutorials Cpluspluscom. Do while just in c example program. -
GC Assertions: Using the Garbage Collector to Check Heap Properties
GC Assertions: Using the Garbage Collector to Check Heap Properties Edward Aftandilian Samuel Z. Guyer Tufts University Tufts University 161 College Ave 161 College Ave Medford MA Medford MA [email protected] [email protected] ABSTRACT The goal of this work is to develop a general mechanism, which GC assertion ex- This paper introduces GC assertions, a system interface that pro- we call a , that allows the programmer to express pected grammers can use to check for errors, such as data structure in- properties of data structures and convey this information to variant violations, and to diagnose performance problems, such as the garbage collector. The key is that many useful, non-trivial prop- memory leaks. GC assertions are checked by the garbage collec- erties can be easily checked by the garbage collector at GC time, tor, which is in a unique position to gather information and answer imposing little or no run-time overhead. The collector triggers the questions about the lifetime and connectivity of objects in the heap. assertion if it finds that the expected properties have been violated. We introduce several kinds of GC assertions, and we describe how While GC assertions require extra programmer effort, they cap- they are implemented in the collector. We also describe our report- ture application-specific properties that programmers want to check ing mechanism, which provides a complete path through the heap and can easily express. For example, we provide a GC assertion to to the offending objects. We show results for one type of asser- verify that a data structure is reclaimed at the next collection when tion that allows the programmer to indicate that an object should the programmer expects it to be garbage. -
Heapviz: Interactive Heap Visualization for Program Understanding and Debugging
Heapviz: Interactive Heap Visualization for Program Understanding and Debugging 1 Abstract Understanding the data structures in a program is crucial to understanding how the program works, or why it doesn’t work. Inspecting the code that implements the data structures, however, is an arduous task and often fails to yield insights into the global organization of a program’s data. Inspecting the actual contents of the heap solves these problems but presents a significant challenge of its own: finding an effective way to present the enormous number of objects it contains. In this paper we present Heapviz, a tool for visualizing and exploring snapshots of the heap obtained from a running Java program. Unlike existing tools, such as tra- ditional debuggers, Heapviz presents a global view of the program state as a graph, together with powerful interactive capabilities for navigating it. Our tool employs sev- eral key techniques that help manage the scale of the data. First, we reduce the size and complexity of the graph by using algorithms inspired by static shape analysis to aggregate the nodes that make up a data structure. Second, we implement a power- ful visualization component whose interactive interface provides extensive support for exploring the graph. The user can search for objects based on type, connectivity, and field values; group objects; and color or hide and show each group. The user may also inspect individual objects to see their field values and neighbors in the graph. These interactive abilities help the user manage the complexity of these huge graphs. By applying Heapviz to both constructed and real-world examples, we show that Heapviz provides programmers with a powerful and intuitive tool for exploring program behavior. -
Detecting and Escaping Infinite Loops Using Bolt
Detecting and Escaping Infinite Loops Using Bolt by Michael Kling Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Masters of Engineering in Electical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2012 c Massachusetts Institute of Technology 2012. All rights reserved. Author.............................................................. Department of Electrical Engineering and Computer Science February 1, 2012 Certified by. Martin Rinard Professor Thesis Supervisor Accepted by . Prof. Dennis M. Freeman Chairman, Masters of Engineering Thesis Committee 2 Detecting and Escaping Infinite Loops Using Bolt by Michael Kling Submitted to the Department of Electrical Engineering and Computer Science on February 1, 2012, in partial fulfillment of the requirements for the degree of Masters of Engineering in Electical Engineering and Computer Science Abstract In this thesis we present Bolt, a novel system for escaping infinite loops. If a user suspects that an executing program is stuck in an infinite loop, the user can use the Bolt user interface, which attaches to the running process and determines if the program is executing in an infinite loop. If that is the case, the user can direct the interface to automatically explore multiple strategies to escape the infinite loop, restore the responsiveness of the program, and recover useful output. Bolt operates on stripped x86 and x64 binaries, analyzes both single-thread and multi-threaded programs, dynamically attaches to the program as-needed, dynami- cally detects the loops in a program and creates program state checkpoints to enable exploration of different escape strategies. This makes it possible for Bolt to detect and escape infinite loops in off-the-shelf software, without available source code, or overhead in standard production use. -
CS 161, Lecture 8: Error Handling and Functions – 29 January 2018 Revisit Error Handling
CS 161, Lecture 8: Error Handling and Functions – 29 January 2018 Revisit Error Handling • Prevent our program from crashing • Reasons programs will crash or have issues: • Syntax Error – prevents compilation, the programmer caused this by mistyping or breaking language rules • Logic Errors – the code does not perform as expected because the underlying logic is incorrect such as off by one, iterating in the wrong direction, having conditions which will never end or be met, etc. • Runtime Errors – program stops running due to segmentation fault or infinite loop potentially caused by trying to access memory that is not allocated, bad user input that was not handled, etc. check_length • The end of a string is determined by the invisible null character ‘\0’ • while the character in the string is not null, keep counting Assignment 3 Notes • Allowed functions: • From <string>: .length(), getline(), [], += • From <cmath>: pow() • Typecasting allowed only if the character being converted fits the stated criterion (i.e. character was confirmed as an int, letter, etc.) • ASCII Chart should be used heavily http://www.asciitable.com/ Debugging Side Bar • Read compiler messages when you have a syntax error • If you suspect a logic error -> print everything! • Allows you to track the values stored in your variables, especially in loops and changing scopes • Gives you a sense of what is executing when in your program Decomposition • Divide problem into subtasks • Procedural Decomposition: get ready in the morning, cooking, etc. • Incremental Programming: -
Efficient and Programmable Support for Memory Access Monitoring And
Appears in the Proceedings of the 13th International Symposium on High-Performance Computer Architecture (HPCA-13), February 2007. MemTracker: Efficient and Programmable Support for Memory Access Monitoring and Debugging ∗ Guru Venkataramani Brandyn Roemer Yan Solihin Milos Prvulovic Georgia Tech Georgia Tech North Carolina State University Georgia Tech [email protected] [email protected] [email protected] [email protected] Abstract many of these tasks, performance overheads of such tools are prohibitive, especially in post-deployment monitoring of live Memory bugs are a broad class of bugs that is becoming (production) runs where end users are directly affected by the increasingly common with increasing software complexity, overheads of the monitoring scheme. and many of these bugs are also security vulnerabilities. Un- One particularly important and broad class of program- fortunately, existing software and even hardware approaches ming errors is erroneous use or management of memory for finding and identifying memory bugs have considerable (memory bugs). This class of errors includes pointer arith- performance overheads, target only a narrow class of bugs, metic errors, use of dangling pointers, reads from unini- are costly to implement, or use computational resources in- tialized locations, out-of-bounds accesses (e.g. buffer over- efficiently. flows), memory leaks, etc. Many software tools have been This paper describes MemTracker, a new hardware sup- developed to detect some of these errors. For example, Pu- port mechanism that can be configured to perform different rify [8] and Valgrind [13] detect memory leaks, accesses to kinds of memory access monitoring tasks. MemTracker as- unallocated memory, reads from uninitialized memory, and sociates each word of data in memory with a few bits of some dangling pointer and out-of-bounds accesses. -
Automatic Repair of Infinite Loops
Automatic Repair of Infinite Loops Sebastian R. Lamelas Marcote Martin Monperrus University of Buenos Aires University of Lille & INRIA Argentina France Abstract Research on automatic software repair is concerned with the develop- ment of systems that automatically detect and repair bugs. One well-known class of bugs is the infinite loop. Every computer programmer or user has, at least once, experienced this type of bug. We state the problem of repairing infinite loops in the context of test-suite based software repair: given a test suite with at least one failing test, generate a patch that makes all test cases pass. Consequently, repairing infinites loop means having at least one test case that hangs by triggering the infinite loop. Our system to automatically repair infinite loops is called Infinitel. We develop a technique to manip- ulate loops so that one can dynamically analyze the number of iterations of loops; decide to interrupt the loop execution; and dynamically examine the state of the loop on a per-iteration basis. Then, in order to synthesize a new loop condition, we encode this set of program states as a code synthesis problem using a technique based on Satisfiability Modulo Theory (SMT). We evaluate our technique on seven seeded-bugs and on seven real-bugs. Infinitel is able to repair all of them, within seconds up to one hour on a standard laptop configuration. 1 Introduction Research on automatic software repair is concerned with the development of sys- tems that automatically detect and repair bugs. We consider as bug a behavior arXiv:1504.05078v1 [cs.SE] 20 Apr 2015 observed during program execution that does not correspond to the expected one. -
Chapter 6 Flow of Control
Chapter 6 Flow of Control 6.1 INTRODUCTION “Don't you hate code that's In Figure 6.1, we see a bus carrying the children to not properly indented? school. There is only one way to reach the school. The Making it [indenting] part of driver has no choice, but to follow the road one milestone the syntax guarantees that all after another to reach the school. We learnt in Chapter code is properly indented.” 5 that this is the concept of sequence, where Python executes one statement after another from beginning to – G. van Rossum the end of the program. These are the kind of programs we have been writing till now. In this chapter Figure 6.1: Bus carrying students to school » Introduction to Flow of Control Let us consider a program 6-1 that executes in » Selection sequence, that is, statements are executed in an order in which they are written. » Indentation The order of execution of the statements in a program » Repetition is known as flow of control. The flow of control can be » Break and Continue implemented using control structures. Python supports Statements two types of control structures—selection and repetition. » Nested Loops 2021-22 Ch 6.indd 121 08-Apr-19 12:37:51 PM 122 COMPUTER SCIENCE – CLASS XI Program 6-1 Program to print the difference of two numbers. #Program 6-1 #Program to print the difference of two input numbers num1 = int(input("Enter first number: ")) num2 = int(input("Enter second number: ")) diff = num1 - num2 print("The difference of",num1,"and",num2,"is",diff) Output: Enter first number 5 Enter second number 7 The difference of 5 and 7 is -2 6.2 SELECTION Now suppose we have `10 to buy a pen. -
Secure Virtual Architecture: Using LLVM to Provide Memory Safety to the Entire Software Stack
Secure Virtual Architecture: Using LLVM to Provide Memory Safety to the Entire Software Stack John Criswell, University of Illinois Andrew Lenharth, University of Illinois Dinakar Dhurjati, DoCoMo Communications Laboratories, USA Vikram Adve, University of Illinois What is Memory Safety? Intuitively, the guarantees provided by a safe programming language (e.g., Java, C#) Array indexing stays within object bounds No uses of uninitialized variables All operations are type safe No uses of dangling pointers Control flow obeys program semantics Sound operational semantics Benefits of Memory Safety for Commodity OS Code Security Memory error vulnerabilities in OS kernel code are a reality1 Novel Design Opportunities Safe kernel extensions (e.g. SPIN) Single address space OSs (e.g. Singularity) Develop New Solutions to Higher-Level Security Challenges Information flow policies Encoding security policies in type system 1. Month of Kernel Bugs (http://projects.info-pull.com/mokb/) Secure Virtual Architecture Commodity OS Virtual ISA Compiler + VM Hardware Native ISA Compiler-based virtual machine underneath software stack Uses analysis & transformation techniques from compilers Supports commodity operating systems (e.g., Linux) Typed virtual instruction set enables sophisticated program analysis Provide safe execution environment for commodity OSs Outline SVA Architecture SVA Safety Experimental Results SVA System Architecture Applications Safety Checking Compiler OS Kernel Drivers OS Memory Allocator SVA ISA Safety Verifier