More Like this

1. Improving Program Matching to Automatically Repair Introductory Programs

   https://doi.org/10.1007/978-3-031-09680-8_30  

   Contractor, Maheen Riaz; Rivero, Carlos R (June 2022, 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
2. Toward a theory of program repair

   https://doi.org/10.1007/s00236-023-00438-4  

   Khaireddine, Besma; Zakharchenko, Aleksandr; Martinez, Matias; Mili, Ali (September 2023, Acta Informatica)

   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
3. Customizing Feedback for Introductory Programming Courses Using Semantic Clusters

   https://doi.org/10.1007/978-3-030-80421-3_30  

   Marin, Victor J.; Hosseini, Hadi; Rivero, Carlos R. (July 2021, Lecture notes in computer science)

   Cristea, Alexandra I.; Troussas, Christos (Ed.)

   The number of introductory programming learners is increasing worldwide. Delivering feedback to these learners is important to support their progress; however, traditional methods to deliver feedback do not scale to thousands of programs. We identify several opportunities to improve a recent data-driven technique to analyze individual program statements. These statements are grouped based on their semantic intent and usually differ on their actual implementation and syntax. The existing technique groups statements that are semantically close, and considers outliers those statements that reduce the cohesiveness of the clusters. Unfortunately, this approach leads to many statements to be considered outliers. We propose to reduce the number of outliers through a new clustering algorithm that processes vertices based on density. Our experiments over six real-world introductory programming assignments show that we are able to reduce the number of outliers and, therefore, increase the total coverage of the programs that are under evaluation. 

   more » « less
4. Traceback: A Fault Localization Technique for Molecular Programs

   Gerten, Michael C; Lathrop, James I; Cohen, Myra B (September 2024, ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA))

   Fault localization is essential to software maintenance tasks such as testing and automated program repair. Many fault localization techniques have been developed, the most common of which are spectrum-based. Most techniques have been designed for traditional programming paradigms that map passing and failing test cases to lines or branches of code, hence specialized programming paradigms which utilize different code abstractions may fail to localize well. In this paper, we study fault localization in the context of a class of programs, molecular programs. Recent research has designed automated testing and repair frameworks for these programs but has ignored the importance of fault localization. As we demonstrate, using existing spectrum-based approaches may not provide much information. Instead we propose a novel approach, Traceback, that leverages temporal trace data. In an empirical study on a set of 89 faulty program variants, we demonstrate that Traceback provides between a 32-90\% improvement in localization over reaction-based mapping, a direct translation of spectrum-based localization. We see little difference in parameter tuning of Traceback when all tests, or only code-based (invariant) tests are used, however the best depth and weight parameters vary when using specification based tests, which can be either functional or metamorphic. Overall, invariant-based tests provide the best localization results (either alone or in combination with others), followed by metamorphic and then functional tests. 

   more » « less
5. Generating and Analyzing Program Call Graphs using Ontology

   https://doi.org/10.1109/ProTools56701.2022.00008  

   Dorta, Ethan; Yan, Yonghong; Liao, Chunhua (November 2022, 2022 IEEE/ACM Workshop on Programming and Performance Visualization Tools (ProTools))

   Call graph or caller-callee relationships have been used for various kinds of static program analysis, performance analysis and profiling, and for program safety or security analysis such as detecting anomalies of program execution or code injection attacks. However, different tools generate call graphs in different formats, which prevents efficient reuse of call graph results. In this paper, we present an approach of using ontology and resource description framework (RDF) to create knowledge graphs for specifying call graphs to facilitate the construction of full-fledged and complex call graphs of computer programs, realizing more interoperable and scalable program analyses than conventional approaches. We create a formal ontology-based specification of call graph information to capture concepts and properties of both static and dynamic call graphs so different tools can collaboratively contribute to more comprehensive analysis results. Our experiments show that ontology enables merging of call graphs generated from different tools and flexible queries using a standard query interface. 

   more » « less