skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Toward a theory of program repair
To repair a program does not mean to make it (absolutely) correct; it only means to make it more-correct than it was originally. This is not a mundane academic distinction: Given that programs typically have about a dozen faults per KLOC, it is important for program repair methods and tools to be designed in such a way that they map an incorrect program into a more-correct, albeit still potentially incorrect, program. Yet in the absence of a concept of relative correctness, many program repair methods and tools resort to approximations of absolute correctness; since these methods and tools are often validated against programs with a single fault, making them absolutely correct is indistinguishable from making them more-correct; this has contributed to conceal/ obscure the absence of (and the need for) relative correctness. In this paper we propose a theory of program repair based on a concept of relative correctness. We aspire to encourage researchers in program repair to explicitly specify what concept of relative correctness their method or tool is based upon; and to validate their method or tool by proving that it does enhance relative correctness, as defined.  more » « less
Award ID(s):
2043104
PAR ID:
10538708
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Springer Verlag
Date Published:
Journal Name:
Acta Informatica
Volume:
60
Issue:
3
ISSN:
0001-5903
Page Range / eLocation ID:
209 to 255
Subject(s) / Keyword(s):
program repair absolute correctness relative correctness
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, Lecture notes in computer science)
    Cristea, Alexandra I.; Troussas, Christos (Ed.)
    Supporting novice programming learners at scale has become a necessity. Such a support generally consists of delivering automated feedback on what and why learners did incorrectly. Existing approaches cast the problem as automatically repairing learners’ incorrect programs; specifically, data-driven approaches assume there exists a correct program provided by other learner that can be extrapolated to repair an incorrect program. Unfortunately, their repair potential, i.e., their capability of providing feedback, is hindered by how they compare programs. In this paper, we propose a flexible program alignment based on program dependence graphs, which we enrich with semantic information extracted from the programs, i.e., operations and calls. Having a correct and an incorrect graphs, we exploit approximate graph alignment to find correspondences at the statement level between them. Each correspondence has a similarity attached to it that reflects the matching affinity between two statements based on topology (control and data flow information) and semantics (operations and calls). Repair suggestions are discovered based on this similarity. We evaluate our flexible approach with respect to rigid schemes over correct and incorrect programs belonging to nine real-world introductory programming assignments. We show that our flexible program alignment is feasible in practice, achieves better performance than rigid program comparisons, and is more resilient when limiting the number of available correct programs. 
    more » « less
  2. ; ; ; ; ; (, Science of Computer Programming)
    Context. Several Research areas emerged and have been proceeding independently when in fact they have much in common. These include: mutant subsumption and mutant set minimization; relative correctness and the semantic definition of faults; differentiator sets and their application to test diversity; generate-and--validate methods of program repair; test suite coverage metrics. Objective. Highlight their analogies, commonalities and overlaps; explore their potential for synergy and shared research goals; unify several disparate concepts around a minimal set of artifacts. Method. Introduce and analyze a minimal set of concepts that enable us to model these disparate research efforts, and explore how these models may enable us to share insights between different research directions, and advance their respective goals. Results. Capturing absolute (total and partial) correctness and relative (total and partial) correctness with a single concept: Detector sets. Using the same concept to quantify the effectiveness of test suites, and prove that the proposed measure satisfies appealing monotonicity properties. Using the measure of test suite effectiveness to model mutant set minimization as an optimization problem, characterized by an objective function and a constraint. Generalizing the concept of mutant subsumption using the concept of differentiator sets. Identifying analogies between detector sets and differentiator sets, and inferring relationships between subsumption and relative correctness. Conclusion. This paper does not aim to answer any pressing research question as much as it aims to raise research questions that use the insights gained from one research venue to gain a fresh perspective on a related research issue. mutant subsumption; mutant set minimization; relative correctness; absolute correctness; total correctness; partial correctness; program fault; program repair; differentiator set; detector set. 
    more » « less
  3. ; (, Springer)
    Crossley, Scott; Popescu, Elvira (Ed.)
    Automated program repair is a promising approach to deliver feedback to novice learners at scale. CLARA is an effective repairer that uses a correct program to fix an incorrect program. CLARA suffers from two main issues: rigid matching and lack of support for typical constructs and tasks in introductory programming assignments. We present several modifications to CLARA to overcome these problems. We propose approximate graph matching based on semantic and topological information of the programs compared, and modify CLARA’s abstract syntax tree processor and interpreter to support new constructs and tasks like reading from/writing to console. Our experiments show that, thanks to our modifications, we can apply CLARA to real-world programs. Also, our approximate graph matching allows us to repair many incorrect programs that are not repaired using rigid program matching. 
    more » « less
  4. ; ; ; (, Proceedings of the International Conference on emerging Networking EXperiments and Technologies)
    Recent trends in software-defined networking have extended network programmability to the data plane. Unfortunately, the chance of introducing bugs increases significantly. Verification can help prevent bugs by assuring that the program does not violate its requirements. Although research on the verification of P4 programs is very active, we still need tools to make easier for programmers to express properties and to rapidly verify complex invariants. In this paper, we leverage assertions and symbolic execution to propose a more general P4 verification approach. Developers annotate P4 programs with assertions expressing general network correctness properties; the result is transformed into C models and all possible paths symbolically executed. We implement a prototype, and use it to show the feasibility of the verification approach. Because symbolic execution does not scale well, we investigate a set of techniques to speed up the process for the specific case of P4 programs. We use the prototype implemented to show the gains provided by three speed up techniques (use of constraints, program slicing, parallelization), and experiment with different compiler optimization choices. We show our tool can uncover a broad range of bugs, and can do it in less than a minute considering various P4 applications. 
    more » « less
  5. ; ; ; ; ; (, IEEE Symposium on Security and Privacy)
    Software APIs exhibit rich diversity and complexity which not only renders them a common source of programming errors but also hinders program analysis tools for checking them. Such tools either expect a precise API specification, which requires program analysis expertise, or presume that correct API usages follow simple idioms that can be automatically mined from code, which suffers from poor accuracy. We propose a new approach that allows regular programmers to find API misuses. Our approach interacts with the user to classify valid and invalid usages of each target API method. It minimizes user burden by employing an active learning algorithm that ranks API usages by their likelihood of being invalid. We implemented our approach in a tool called ARBITRAR for C/C++ programs, and applied it to check the uses of 18 API methods in 21 large real-world programs, including OpenSSL and Linux Kernel. Within just 3 rounds of user interaction on average per API method, ARBITRAR found 40 new bugs, with patches accepted for 18 of them. Moreover, ARBITRAR finds all known bugs reported by a state-of-the-art tool APISAN in a benchmark suite comprising 92 bugs with a false positive rate of only 51.5% compared to APISAN’s 87.9% 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email