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44th IEEE Security and Privacy Workshops (SPW)

作者： Kaoudis, Kelly Brodin, Henrik Sultanik, Evan Trail Bits New York NY 10003 USA

ISBN: (纸本)9798350312362

Subtle bugs that only manifest in certain software configurations are notoriously difficult to correctly trace. Sometimes called Heisenbugs, these runtime variability flaws can result from invoking undefined behavior in languages like C and C++, or from compiler flaws. In this paper, we present a novel analysis technique for detecting and correctly diagnosing variability bugs' impact on a program through comparing control-affecting data flow across differently compiled program variants. Our UBet prototype dynamically derives a runtime control flow trace while tracing universal data flow for a program processing a given input, operating at a level of tracing completeness not achievable through similar dynamic instrumentation means. Sans compiler bugs or undefined behavior, every compile-time program configuration (i.e., compiler flags vary) should be semantically equivalent. Thus, any input for which a program variant produces inconsistent output indicates a variability bug. Our analysis compares control-affecting data flow traces from disagreeing program version runs to identify related input bytes and determine where in the program the processing variability originates. Though we initially demonstrate our technique on C++ variability bugs in Nitro, the American Department of Defense NITF (National Imagery Transmission Format) reference implementation parser, our approach applies equally to other programs and input types beyond NITF parsers. Finally, we sketch a path toward completing this work and refining our analysis, including evaluating parsers of other input formats.

关键词： application security dynamic analysis dynamic tainting validation testing and debugging

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Improving Spectrum-Based Localization of Multiple Faults by Iterative Test Suite Reduction 2023

Improving Spectrum-Based Localization of Multiple Faults by ...

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32nd ACM SIGSOFT International Symposium on Software testing and Analysis (ISSTA)

作者： Callaghan, Dylan Fischer, Bernd Stellenbosch Univ Stellenbosch South Africa

ISBN: (纸本)9798400702211

Spectrum-based fault localization (SBFL) works well for single-fault programs but its accuracy decays for increasing fault numbers. We present FLITSR (Fault Localization by Iterative Test Suite Reduction), a novel SBFL extension that improves the localization of a given base metric specifically in the presence of multiple faults. FLITSR iteratively selects reduced versions of the test suite that better localize the individual faults in the system. This allows it to identify and re-rank faults ranked too low by the base metric because they were masked by other program elements. We evaluated FLITSR over method-level spectra from an existing large synthetic dataset comprising 75000 variants of 15 opensource projects with up to 32 injected faults, as well as method- and statement-level spectra from a new dataset with 326 true multi-fault versions from the Defects4J benchmark set containing up to 14 real faults. For all three spectrum types we consistently see substantial reductions of the average wasted efforts at different fault levels, of 30%-90% over the best base metric, and generally similarly large increases in precision and recall, albeit with larger variance across the underlying projects. For the method-level real faults, FLITSR also substantially outperforms GRACE, a state-of-the-art learning-based fault localizer.

关键词： testing and debugging Spectrum-based fault localization

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On the Suitability of LSP and DAP for Domain-Specific Languages

On the Suitability of LSP and DAP for Domain-Specific Langua...

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ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS)

作者： Enet, Josselin Bousse, Erwan Tisi, Massimo Sunye, Gerson Nantes Univ CNRS Ecole Cent Nantes IMT AtlantiqueLS2NUMR 6004 Nantes France

ISBN: (纸本)9798350324983

Domain-Specific Languages (DSLs) help manage the growing complexity of systems by facilitating their description and execution or simulation via tailored languages. A large part of the development costs of a DSL comes from building the associated tools it requires, such as an editor or a debugger. To reduce these costs, the Language Server Protocol (LSP) and Debug Adapter Protocol (DAP) enable the creation of generic tooling interfaces which rely on standardized services exposed by languages. However, as these protocols have been designed for General Purpose Languages (GPLs), their applicability to DSLs has no yet been extensively studied. In this paper, we analyze both LSP and DAP, with an emphasis regarding their relevance for the development of tooling for DSLs. We provide both a high-level insight into these protocols, such as a dependency graph of their services, and a more fine-grained qualitative analysis of each service. We show that while some services defined by these two protocols can be provided by any DSL, others make strong assumptions on the concepts that should be part of the considered DSL. Conversely, domain-specific concepts available in some DSLs are not exploitable through these protocols, thus reducing the capabilities of generic tools.

关键词： Programming Languages and Software Algorithm/protocol design and analysis testing and debugging Document and Text Editing

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Jdebug: A Fast, Non-Intrusive and Scalable Fault Locating Tool for Ten-Million-Scale Parallel Applications

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第12期33卷 3491-3504页

作者： Peng, Dajia Feng, Yunlong Liu, Yong Liu, Xin Xue, Wei Chen, Dexun Song, Jiawei Chen, Zuoning Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Natl Supercomp Ctr Wuxi Wuxi Jiangsu Peoples R China Natl Res Ctr Parallel Comp Engn & Technol Beijing 100083 Peoples R China

This article presents Jdebug, a fast, non-intrusive and scalable fault locating tool for extreme-scale parallel applications. Large-scale debugging has drawn more attention with the increasing scale of supercomputers and applications. To eliminate program intrusion caused by traditional instrumentation or interception during debugging information acquisition, we introduce the out-of-band management into large-scale debugging. We propose a rapid information gathering scheme that separates user and debugging traffic to solve scalability problem and to eliminate program interference during merging data. Observations of Program Counters (PC) and performance characteristics in suspended applications find abnormalities and help locate abnormal threads caused by software errors or hardware failures effectively. Evaluation shows that Jdebug collects PCs of over 20 million cores on the new Sunway supercomputer within 1.97 seconds, and can locate the abnormal threads in 1.4 seconds with an accuracy of 92.5%. In the running test of three fundamental benchmarks (HPL, HPCG, Graph500) and seventeen real-world applications, Jdebug quickly and accurately locates abnormal threads to help find scalability errors and hardware failures including memory access failures, communication failures, and execution component failures, which validates its effectiveness.

关键词： debugging Servers Supercomputers Hardware Maintenance engineering Instruction sets Task analysis Distributed debugging software engineering software software engineering testing and debugging

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Developing Computational Thinking Practices in Primary Education. Outcomes from a School-Year Instructional Intervention 3rd

Developing Computational Thinking Practices in Primary Educa...

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3rd International Conference on Technology and Innovation in Learning, Teaching and Education (TECH-EDU)

作者： Vourletsis, Ioannis Politis, Panagiotis Univ Thessaly Dept Primary Educ Volos Greece

ISBN: (纸本)9783031229176;9783031229183

Computational Thinking (CT), a problem-solving mental activity primarily dependent on abstraction, is regarded as a critical skill set for 21st-century students. Recent academic debates have led to a general agreement about the definition of Computational Thinking and the broad set of knowledge and skills that encloses, which is categorized into concepts, practices, and perspectives. The purpose of this study is to examine the effect of a Computational Thinking-based instructional intervention on the development of primary school students' computational practices. We employed a mixed methods approach and the research sample included 103 primary school students who collaborated in pairs during the regular teaching hour assigned to Information Technology throughout a full school year. Our results suggest that the proposed instructional intervention may enhance students' perceptions of their problem-solving skills. They also show that students' participation in testing and debugging as well as reusing and remixing digital games' code may improve their proficiency level regarding these Computational Thinking practices. Overall, despite its limitations, our study strengthens the hypothesis for the effectiveness of programming to develop primary school students' Computational Thinking.

关键词： Computational Thinking (CT) Visual programming Problem solving testing and debugging Reusing and remixing

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Test Case Aggregation for Efficient Feature Model testing 22

Test Case Aggregation for Efficient Feature Model Testing

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26th ACM International Systems and Software Product Line Conference (SPLC)

作者： Le, Viet-Man Felfernig, Alexander Tran, Thi Ngoc Trang Graz Univ Technol Graz Austria

ISBN: (纸本)9781450392068

The increasing size and complexity of feature models (FM) requires the provision of efficient testing and debugging techniques. Feature models can be tested, for example, with regard to their conformance with a pre-defined set of analysis operations. In this paper, we show how the number of consistency checks for FM testing can be reduced on the basis of test case aggregation. Using a divideand-conquer based approach, we show how to transform a feature model test suite into a corresponding aggregated representation where individual test cases can be combined if specific consistency criteria are fulfilled. Performance improvements are also analyzed on the basis of a best- and worst-case runtime analysis.

关键词： Variability Modeling testing and debugging

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WipeOutR: Automated Redundancy Detection for Feature Models 22

WipeOutR: Automated Redundancy Detection for Feature Models

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26th ACM International Systems and Software Product Line Conference (SPLC)

作者： Le, Viet-Man Felfernig, Alexander Uta, Mathias Tran, Thi Ngoc Trang Silva, Cristian Vidal Graz Univ Technol Graz Austria Siemens Energy AG Erlangen Germany Univ Talca Talca Chile

ISBN: (纸本)9781450394437

Feature models are used to specify variability and commonality properties of software artifacts. In order to assure high-quality models, different feature model analysis and testing operations can be applied. In this paper, we present two new algorithms that help to make feature model configuration as well as different kinds of analysis operations more efficient. Specifically, we focus on the automated identification of redundancies in feature models and corresponding test suites. Redundant constraints in feature models can lead to low-performing configuration (solution) search and also to additional efforts in feature model debugging. Redundant feature model test cases can trigger inefficiencies in testing operations. In this paper, we introduce WipeOutR which is an algorithmic approach to support the automated identification of redundancies. This approach has the potential to significantly improve the quality of feature model development and configuration.

关键词： Variability Modeling Feature Models Quality Assurance testing and debugging Redundancy Detection

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XAI4FL: Enhancing Spectrum-Based Fault Localization with Explainable Artificial Intelligence 30

XAI4FL: Enhancing Spectrum-Based Fault Localization with Exp...

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30th IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Widyasari, Ratnadira Prana, Gede Artha Azriadi Haryono, Stefanus A. Tiant, Yuan Zachiary, Hafil Noer Lo, David Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore Queens Univ Sch Comp Kingston ON Canada

ISBN: (纸本)9781450392983

Manually finding the program unit (e.g., class, method, or statement) responsible for a fault is tedious and time-consuming. To mitigate this problem, many fault localization techniques have been proposed. A popular family of such techniques is spectrum-based fault localization (SBFL), which takes program execution traces (spectra) of failed and passed test cases as input and applies a ranking formula to compute a suspiciousness score for each program unit. However, most existing SBFL techniques fail to consider two facts: 1) not all failed test cases contribute equally to a considered fault(s), and 2) program units collaboratively contribute to the failure/pass of each test case in different ways. In this study, we propose a novel idea that first models the SBFL task as a classification problem of predicting whether a test case will fail or pass based on spectra information on program units. We subsequently apply eXplainable Artificial Intelligence (XAI) techniques to infer the local importance of each program unit to the prediction of each executed test case. Applying XAI to the failed test case, we retrieve information about which program statements within the test case that are considered the most important (i.e., have the biggest effect in making the test case failed). Such a design can automatically learn the unique contributions of failed test cases to the suspiciousness of a program unit by learning the different and collaborative contributions of program units to each test case's executed result. As far as we know, this is the first XAI-supported SBFL approach. We evaluate the new approach on the Defects4J benchmark dataset. We compare the performance of our approach against five popular SBFL techniques: DStar, Tarantula, Barinel, Ochiai, and O-P. We measure their performance using the Top-K and EXAM scores. In particular, we focus on the result of the Top-1, which importance has been highlighted in automated program repair domain, where the proposed method

关键词： Fault Localization Explainable Artificial Intelligence (XAI) Model-Agnostic Explanation Technique Spectrum-based Fault Localization testing and debugging

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Effective Lightweight Software Fault Localization Combined with Fault Context and Test Suite Optimization

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ENGINEERING LETTERS 2024年第4期32卷 835-870页

作者： Saxena, Amol Bhatnagar, Roheet Srivastava, Devesh Kumar Manipal Univ Jaipur Comp Sci & Engn Dept SCSE Jaipur 303007 Rajasthan India Manipal Univ Jaipur Dept Informat Technol SIT Jaipur 303007 Rajasthan India

locating software faults during debugging is crucial yet challenging. Automated techniques, such as spectrum -based fault localization (SBFL), aid developers in efficiently localizing faults by analyzing program execution information and utilizing statistical approaches to rank program entities according to their suspiciousness. SBFL is also known as lightweight fault localization because of its scalability and minimal computational overhead. While essential, there has been limited research on how test suites affect fault localization. In this paper, we show how test suites impact fault localization and how they can be optimized for better results. SBFL techniques have some inherent limitations, especially in diagnosing faults within loop bodies or iteration statements. Additionally, identical suspiciousness levels can result in ties. While SBFL techniques effectively rank faulty program entities among the Top -N suspicious entities, they might not consistently position the faulty entity within the initial few positions. To address these research gaps, this paper proposes a hybrid approach that combines test suite optimization, statement execution frequency, and fault context concepts to enhance the performance of existing SBFL techniques in single fault scenarios. We evaluate our approach using three popular SBFL methods (Ochiai, Jaccard, and DStar) on Siemens benchmarks and four large real -world programs (flex, grep, sed, and space) with their test suites. The results demonstrate a significant enhancement in fault localization performance when applying our proposed approach to existing SBFL methods. For example, when applied to Ochiai, it reduces examined statements by 62.76% and 65.23% on average for the two test suites, respectively. Furthermore, it identifies 52% of faults by examining only 1% or less of the code and locates 60% of faults by analyzing only 0.1% or less of the code in Siemens and four large real -world programs, respectively. Similar improvemen

关键词： fault context program spectrum spectrum- based fault localization statement execution frequency suspiciousness testing and debugging test suite optimization

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JIFFY: A Framework for Encompassing Aspects in testing and debugging Software

JIFFY: A Framework for Encompassing Aspects in Testing and D...

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8th International Workshop on Automation of Software Test (AST)

作者： Asif, Mohammad Reddy, Y. Raghu Int Inst Informat Technol IIIT H Software Engn Res Lab Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467361613

Aspect Oriented Programming (AOP) advocates the notion of aspects to encapsulate crosscutting concerns. A concern is a behavior in a computer program and is said to be crosscutting if the module(s) that address the behavior are scattered and tangled with other modules of the system. In this paper, we investigate the possibility of using AOP for software testing and non-invasive debugging. We have identified some crosscutting concerns like access control, logging, performance, tracing, etc., and developed a framework based on java bytecode instrumentation technique to inject these crosscutting concerns into the compiled code. The framework is available as a service, i.e., the service takes the required code as input and produces the changed code as output that contains the appropriate aspects. Thus, it is argued that the framework can be the basis for implementing the notion of "Enabling testing as a Service".

关键词： testing and debugging Aspect Oriented Programming Byte code instrumentation Unit testing

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