Dan Hao

Ke Sun, Di Wang, Yuyan Bao et al.

Intrinsic definitional interpreters, definitional interpreters that operate on typing derivations instead of abstract syntax trees, have recently been studied as a promising methodology for defining dynamic semantics of programming languages. A key benefit is that type safety interactively guides and constrains the interpreter's construction. Analogously to typing relations, Hoare logic is widely used to guarantee program correctness. Can intrinsic definitional interpreters be realized to operate over Hoare-logic derivations? We explore this question in depth by developing definitional interpreters in Rocq for (i) a basic Hoare logic, and (ii) a realistic logic featuring heaps, dynamic-frame-based local reasoning, well-founded functions, and behavioral subtyping. Central to our approach is a novel technique we call entry-indexing, which we use to interpret total-correctness derivations and well-founded functions. Our second development yields, to our knowledge, the first formalization of a dynamic-frame-based Hoare logic with well-founded functions, behavioral subtyping, and total correctness, as well as the first fully mechanized Hoare logic with dynamic frames.

Yakun Zhang, Chen Liu, Xiaofei Xie et al.

GUI test migration aims to produce test cases with events and assertions to test specific functionalities of a target app. Existing migration approaches typically focus on the widget-mapping paradigm that maps widgets from source apps to target apps. However, since different apps may implement the same functionality in different ways, direct mapping may result in incomplete or buggy test cases, thus significantly impacting the effectiveness of testing target functionality and the practical applicability of migration approaches. In this paper, we propose a new migration paradigm (i.e., the abstraction-concretization paradigm) that first abstracts the test logic for the target functionality and then utilizes this logic to generate the concrete GUI test case. Furthermore, we introduce MACdroid, the first approach that migrates GUI test cases based on this paradigm. Specifically, we propose an abstraction technique that utilizes source test cases from source apps targeting the same functionality to extract a general test logic for that functionality. Then, we propose a concretization technique that utilizes the general test logic to guide an LLM in generating the corresponding GUI test case (including events and assertions) for the target app. We evaluate MACdroid on two widely-used datasets (including 31 apps, 34 functionalities, and 123 test cases). On the FrUITeR dataset, the test cases generated by MACdroid successfully test 64% of the target functionalities, improving the baselines by 191%. On the Lin dataset, MACdroid successfully tests 75% of the target functionalities, outperforming the baselines by 42%. These results underscore the effectiveness of MACdroid in GUI test migration.

Yiling Lou, Jun Yang, Samuel Benton et al.

In recent years, Automated Program Repair (APR) has been extensively studied in academia and even drawn wide attention from industry. However, APR techniques can be extremely time consuming since (1) a large number of patches can be generated for a given bug, and (2) each patch needs to be executed on the original tests to ensure its correctness. In the literature, various techniques (e.g., based on learning, mining, and constraint solving) have been proposed/studied to reduce the number of patches. Intuitively, every patch can be treated as a software revision during regression testing; thus, traditional Regression Test Selection (RTS) techniques can be leveraged to only execute the tests affected by each patch (as the other tests would keep the same outcomes) to further reduce patch execution time. However, few APR systems actually adopt RTS and there is still a lack of systematic studies demonstrating the benefits of RTS and the impact of different RTS strategies on APR. To this end, this paper presents the first extensive study of widely-used RTS techniques at different levels (i.e., class/method/statement levels) for 12 state-of-the-art APR systems on over 2M patches. Our study reveals various practical guidelines for bridging the gap between APR and regression testing.

Yiling Lou, Ali Ghanbari, Xia Li et al.

Software bugs are prevalent in modern software systems and notoriously hard to debug manually. Therefore, a large body of research efforts have been dedicated to automated software debugging, including both automated fault localization and program repair. However, the existing fault localization techniques are usually ineffective 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, we observe that in the literature their only connection 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 explore their connection in the other direction, 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, a simplistic approach using 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 localize 161 of the 395 studied bugs within Top-1, while state-of-the-art spectrum and mutation based fault localization techniques at most localize 117 within Top-1. We also demonstrate ProFL's effectiveness under different settings. Lastly, we show that ProFL can further boost state-of-the-art fault localization via both unsupervised and supervised learning.

Jie M. Zhang, Feng Li, Dan Hao et al.

This paper presents a large-scale study that investigates the bug resolution characteristics among popular Github projects written in different programming languages. We explore correlations but, of course, we cannot infer causation. Specifically, we analyse bug resolution data from approximately 70 million Source Line of Code, drawn from 3 million commits to 600 GitHub projects, primarily written in 10 programming languages. We find notable variations in apparent bug resolution time and patch (fix) size. While interpretation of results from such large-scale empirical studies is inherently difficult, we believe that the differences in medians are sufficiently large to warrant further investigation, replication, re-analysis and follow up research. For example, in our corpus, the median apparent bug resolution time (elapsed time from raise to resolve) for Ruby was 4X that for Go and 2.5X for Java. We also found that patches tend to touch more files for the corpus of strongly typed and for statically typed programs. However, we also found evidence for a lower elapsed resolution time for bug resolution committed to projects constructed from statically typed languages. These findings, if replicated in subsequent follow on studies, may shed further empirical light on the debate about the importance of static typing.

Jie Zhang, Shi Han, Dan Hao et al.

Spreadsheets are the most popular end-user programming software, where formulae act like programs and also have smells. One well recognized common smell of spreadsheet formulae is nest-IF expressions, which have low readability and high cognitive cost for users, and are error-prone during reuse or maintenance. However, end users usually lack essential programming language knowledge and skills to tackle or even realize the problem. The previous research work has made very initial attempts in this aspect, while no effective and automated approach is currently available. This paper firstly proposes an AST-based automated approach to systematically refactoring nest-IF formulae. The general idea is two-fold. First, we detect and remove logic redundancy on the AST. Second, we identify higher-level semantics that have been fragmented and scattered, and reassemble the syntax using concise built-in functions. A comprehensive evaluation has been conducted against a real-world spreadsheet corpus, which is collected in a leading IT company for research purpose. The results with over 68,000 spreadsheets with 27 million nest-IF formulae reveal that our approach is able to relieve the smell of over 99\% of nest-IF formulae. Over 50% of the refactorings have reduced nesting levels of the nest-IFs by more than a half. In addition, a survey involving 49 participants indicates that for most cases the participants prefer the refactored formulae, and agree on that such automated refactoring approach is necessary and helpful.

Ming Wen, Junjie Chen, Rongxin Wu et al.

Automated program repair (APR) has attracted great research attention, and various techniques have been proposed. Search-based APR is one of the most important categories among these techniques. Existing researches focus on the design of effective mutation operators and searching algorithms to better find the correct patch. Despite various efforts, the effectiveness of these techniques are still limited by the search space explosion problem. One of the key factors attribute to this problem is the quality of fault spaces as reported by existing studies. This motivates us to study the importance of the fault space to the success of finding a correct patch. Our empirical study aims to answer three questions. Does the fault space significantly correlate with the performance of search-based APR? If so, are there any indicative measurements to approximate the accuracy of the fault space before applying expensive APR techniques? Are there any automatic methods that can improve the accuracy of the fault space? We observe that the accuracy of the fault space affects the effectiveness and efficiency of search-based APR techniques, e.g., the failure rate of GenProg could be as high as $60\%$ when the real fix location is ranked lower than 10 even though the correct patch is in the search space. Besides, GenProg is able to find more correct patches and with fewer trials when given a fault space with a higher accuracy. We also find that the negative mutation coverage, which is designed in this study to measure the capability of a test suite to kill the mutants created on the statements executed by failing tests, is the most indicative measurement to estimate the efficiency of search-based APR. Finally, we confirm that automated generated test cases can help improve the accuracy of fault spaces, and further improve the performance of search-based APR techniques.

Bo Wang, Yingfei Xiong, Yangqingwei Shi et al.

Mutation analysis has many applications, such as asserting the quality of test suites and localizing faults. One important bottleneck of mutation analysis is scalability. The latest work explores the possibility of reducing the redundant execution via split-stream execution. However, split-stream execution is only able to remove redundant execution before the first mutated statement. In this paper we try to also reduce some of the redundant execution after the execution of the first mutated statement. We observe that, although many mutated statements are not equivalent, the execution result of those mutated statements may still be equivalent to the result of the original statement. In other words, the statements are equivalent modulo the current state. In this paper we propose a fast mutation analysis approach, AccMut. AccMut automatically detects the equivalence modulo states among a statement and its mutations, then groups the statements into equivalence classes modulo states, and uses only one process to represent each class. In this way, we can significantly reduce the number of split processes. Our experiments show that our approach can further accelerate mutation analysis on top of split-stream execution with a speedup of 2.56x on average.