An official website of the United States government
A large body of research efforts have been dedicated to automated software debugging, including both automated fault localization and program repair. However, existing fault localization techniques have limited effectiveness on real-world software systems while even the most advanced program repair techniques can only fix a small ratio of real-world bugs. Although fault localization and program repair are inherently connected, their only existing connection in the literature is that program repair techniques usually use off-the-shelf fault localization techniques (e.g., Ochiai) to determine the potential candidate statements/elements for patching. In this work, we propose the unified debugging approach to unify the two areas in the other direction for the first time, i.e., can program repair in turn help with fault localization? In this way, we not only open a new dimension for more powerful fault localization, but also extend the application scope of program repair to all possible bugs (not only the bugs that can be directly automatically fixed). We have designed ProFL to leverage patch-execution results (from program repair) as the feedback information for fault localization. The experimental results on the widely used Defects4J benchmark show that the basic ProFL can already at least localize 37.61% more bugs within Top-1 than state-of-the-art spectrum and mutation based fault localization. Furthermore, ProFL can boost state-of-the-art fault localization via both unsupervised and supervised learning. Meanwhile, we have demonstrated ProFL's effectiveness under different settings and through a case study within Alipay, a popular online payment system with over 1 billion global users.
more »
« less
This content will become publicly available on September 16, 2025
Traceback: A Fault Localization Technique for Molecular Programs
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
- Award ID(s):
- 1909688
- PAR ID:
- 10512373
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA)
- Subject(s) / Keyword(s):
- fault localization molecular programs chemical reaction networks software debugging
- Format(s):
- Medium: X
- Location:
- Vienna, Austria
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Automated program repair holds the potential to significantly reduce software maintenance effort and cost. However, recent studies have shown that it often produces low-quality patches that repair some but break other functionality. We hypothesize that producing patches by replacing likely faulty regions of code with semantically-similar code fragments, and doing so at a higher level of granularity than prior approaches can better capture abstraction and the intended specification, and can improve repair quality. We create SOSRepair, an automated program repair technique that uses semantic code search to replace candidate buggy code regions with behaviorally-similar (but not identical) code written by humans. SOSRepair is the first such technique to scale to real-world defects in real-world systems. On a subset of the ManyBugs benchmark of such defects, SOSRepair produces patches for 23 (35%) of the 65 defects, including 3, 5, and 8 defects for which previous state-of-the-art techniques Angelix, Prophet, and GenProg do not, respectively. On these 23 defects, SOSRepair produces more patches (8, 35%) that pass all independent tests than the prior techniques. We demonstrate a relationship between patch granularity and the ability to produce patches that pass all independent tests. We then show that fault localization precision is a key factor in SOSRepair's success. Manually improving fault localization allows SOSRepair to patch 24 (37%) defects, of which 16 (67%) pass all independent tests. We conclude that (1) higher-granularity, semantic-based patches can improve patch quality, (2) semantic search is promising for producing high-quality real-world defect repairs, (3) research in fault localization can significantly improve the quality of program repair techniques, and (4) semi-automated approaches in which developers suggest fix locations may produce high-quality patches.more » « less
-
The use of non-traditional computing devices is growing rapidly. One paradigm of interest is chemical reaction networks (CRNs) which can model and use chemical interactions for computation. These CRNs are used to develop programs at the nanoscale for applications such as intelligent drug delivery. In practice, these programs are developed in simulation environments, and then compiled into physical systems. A challenge when designing CRNs for computation is the lack of techniques to verify and validate correctness. In this work, we adapt software testing and repair techniques for use in this domain. In initial work, we designed a testing framework to handle the challenges presented by CRN programs; this includes distributed computation and stochastic behavior. We extended this framework to implement automated program repair of CRN models and automated test generation via program invariants. For future work, we will develop a notion of fault localization for these programs, develop a theory of mutation generation, and address issues regarding flakiness present in this computing paradigm.more » « less
-
null (Ed.)Automated debugging techniques, including fault localization and program repair, have been studied for over a decade. However, the only existing connection between fault localization and program repair is that fault localization computes the potential buggy elements for program repair to patch. Recently, a pioneering work, ProFL, explored the idea of unified debugging to unify fault localization and program repair in the other direction for the first time to boost both areas. More specifically, ProFL utilizes the patch execution results from one state-of-the-art repair system, PraPR, to help improve state-of-the-art fault localization. In this way, ProFL not only improves fault localization for manual repair, but also extends the application scope of automated repair to all possible bugs (not only the small ratio of bugs that can be automatically fixed). However, ProFL only considers one APR system (i.e., PraPR), and it is not clear how other existing APR systems based on different designs contribute to unified debugging. In this work, we perform an extensive study of the unified-debugging approach on 16 state-of-the-art program repair systems for the first time. Our experimental results on the widely studied Defects4J benchmark suite reveal various practical guidelines for unified debugging, such as (1) nearly all the studied 16 repair systems can positively contribute to unified debugging despite their varying repairing capabilities, (2) repair systems targeting multi-edit patches can bring extraneous noise into unified debugging, (3) repair systems with more executed/plausible patches tend to perform better for unified debugging, and (4) unified debugging effectiveness does not rely on the availability of correct patches in automated repair. Based on our results, we further propose an advanced unified debugging technique, UniDebug++, which can localize over 20% more bugs within Top-1 positions than state-of-the-art unified debugging technique, ProFL.more » « less
-
Coverage-based fault localization has been extensively studied in the literature due to its effectiveness and lightweightness for real-world systems. However, existing techniques often utilize coverage in an oversimplified way by abstracting detailed coverage into numbers of tests or boolean vectors, thus limiting their effectiveness in practice. In this work, we present a novel coverage-based fault localization technique, Grace, which fully utilizes detailed coverage information with graph-based representation learning. Our intuition is that coverage can be regarded as connective relationships between tests and program entities, which can be inherently and integrally represented by a graph structure: with tests and program entities as nodes, while with coverage and code structures as edges. Therefore, we first propose a novel graph-based representation to reserve all detailed coverage information and fine-grained code structures into one graph. Then we leverage Gated Graph Neural Network to learn valuable features from the graph-based coverage representation and rank program entities in a listwise way. Our evaluation on the widely used benchmark Defects4J (V1.2.0) shows that Grace significantly outperforms state-of-the-art coverage-based fault localization: Grace localizes 195 bugs within Top-1 whereas the best compared technique can at most localize 166 bugs within Top-1. We further investigate the impact of each Grace component and find that they all positively contribute to Grace. In addition, our results also demonstrate that Grace has learnt essential features from coverage, which are complementary to various information used in existing learning-based fault localization. Finally, we evaluate Grace in the cross-project prediction scenario on extra 226 bugs from Defects4J (V2.0.0), and find that Grace consistently outperforms state-of-the-art coverage-based techniques.more » « less
