An official website of the United States government
Information Retrieval (IR) plays a pivotal role indiverse Software Engineering (SE) tasks, e.g., bug localization and triaging, bug report routing, code retrieval, requirements analysis, etc. SE tasks operate on diverse types of documents including code, text, stack-traces, and structured, semi-structured and unstructured meta-data that often contain specialized vocabularies. As the performance of any IR-based tool critically depends on the underlying document types, and given the diversity of SE corpora, it is essential to understand which models work best for which types of SE documents and tasks.We empirically investigate the interaction between IR models and document types for two representative SE tasks (bug localization and relevant project search), carefully chosen as they require a diverse set of SE artifacts (mixtures of code and text),and confirm that the models’ performance varies significantly with mix of document types. Leveraging this insight, we propose a generalized framework, SRCH, to automatically select the most favorable IR model(s) for a given SE task. We evaluate SRCH w.r.t. these two tasks and confirm its effectiveness. Our preliminary user study shows that SRCH’s intelligent adaption of the IR model(s) to the task at hand not only improves precision and recall for SE tasks but may also improve users’ satisfaction.
more »
« less
On Combining IR Methods to Improve Bug Localization
Information Retrieval (IR) methods have been recently employed to provide automatic support for bug localization tasks. However, for an IR-based bug localization tool to be useful, it has to achieve adequate retrieval accuracy. Lower precision and recall can leave developers with large amounts of incorrect information to wade through. To address this issue, in this paper, we systematically investigate the impact of combining various IR methods on the retrieval accuracy of bug localization engines. The main assumption is that different IR methods, targeting different dimensions of similarity between artifacts, can be used to enhance the confidence in each others' results. Five benchmark systems from different application domains are used to conduct our analysis. The results show that a) near-optimal global configurations can be determined for different combinations of IR methods, b) optimized IR-hybrids can significantly outperform individual methods as well as other unoptimized methods, and c) hybrid methods achieve their best performance when utilizing information-theoretic IR methods. Our findings can be used to enhance the practicality of IR-based bug localization tools and minimize the cognitive overload developers often face when locating bugs.
more »
« less
- Award ID(s):
- 1821525
- PAR ID:
- 10251777
- Date Published:
- Journal Name:
- The 28th International Conference on Program Comprehension
- Page Range / eLocation ID:
- 252 to 262
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
One of the most important tasks related to managing bug reports is localizing the fault so that a fix can be applied. As such, prior work has aimed to automate this task of bug localization by formulating it as an information retrieval problem, where potentially buggy files are retrieved and ranked according to their textual similarity with a given bug report. However, there is often a notable semantic gap between the information contained in bug reports and identifiers or natural language contained within source code files. For user-facing software, there is currently a key source of information that could aid in bug localization, but has not been thoroughly investigated - information from the GUI. We investigate the hypothesis that, for end user-facing applications, connecting information in a bug report with information from the GUI, and using this to aid in retrieving potentially buggy files, can improve upon existing techniques for bug localization. To examine this phenomenon, we conduct a comprehensive empirical study that augments four baseline techniques for bug localization with GUI interaction information from a reproduction scenario to (i) filter out potentially irrelevant files, (ii) boost potentially relevant files, and (iii) reformulate text-retrieval queries. To carry out our study, we source the current largest dataset of fully-localized and reproducible real bugs for Android apps, with corresponding bug reports, consisting of 80 bug reports from 39 popular open-source apps. Our results illustrate that augmenting traditional techniques with GUI information leads to a marked increase in effectiveness across multiple metrics, including a relative increase in Hits@10 of 13-18%. Additionally, through further analysis, we find that our studied augmentations largely complement existing techniques.more » « less
-
Bug tracking systems, which help to track the reported software bugs, have been widely used in software development and maintenance. In these systems, recognizing relevant source files among a large number of source files for a given bug report is a time-consuming and labor-intensive task for software developers. To tackle this problem, information retrieval methods have been widely used to capture either the textual similarities or the semantic similarities between bug reports and source files. However, these two types of similarities are usually considered separately and the historical bug fixings are largely ignored by the existing methods. In this paper, we propose a supervised topic modeling method (STMLOCATOR) for automatically locating the relevant source files for a given bug report. In particular, the proposed model is built upon three key observations. First, supervised modeling can effectively make use of the existing fixing histories. Second, certain words in bug reports tend to appear multiple times in their relevant source files. Third, longer source files tend to have more bugs. By integrating the above three observations, the proposed STMLOCATOR utilizes historical fixings in a supervised way and learns both the textual similarities and semantic similarities between bug reports and source files. We further consider a special type of bug reports with stack-traces in bug reports, and propose a variant of STMLOCATOR to tailor for such bug reports. Experimental evaluations on three real data sets demonstrate that the proposed STMLOCATOR can achieve up to 23.6% improvement in terms of prediction accuracy over its best competitors, and scales linearly with the size of the data. Moreover, the proposed variant further improves STMLOCATOR by up to 76.2% on those bug reports with stack-traces.more » « less
-
In recent years, the MLIR framework has had explosive growth due to the need for extensible deep learning compilers for hardware accelerators. Such examples include Triton, CIRCT, and ONNX-MLIR. MLIR compilers introduce significant complexities in localizing bugs or inefficiencies because of their layered optimization and transformation process with compilation passes. While existing delta debugging techniques can be used to identify a minimum subset of IR code that reproduces a given bug symptom, their naive application to MLIR is time-consuming because real-world MLIR compilers usually involve a large number of compilation passes. Compiler developers must identify a minimized set of relevant compilation passes to reduce the footprint of MLIR compiler code to be inspected for a bug fix. We propose DuoReduce, a dual-dimensional reduction approach for MLIR bug localization. DuoReduce leverages three key ideas in tandem to design an efficient MLIR delta debugger. First, DuoReduce reduces compiler passes that are irrelevant to the bug by identifying ordering dependencies among the different compilation passes. Second, DuoReduce uses MLIR-semantics-aware transformations to expedite IR code reduction. Finally, DuoReduce leverages cross-dependence between the IR code dimension and the compilation pass dimension by accounting for which IR code segments are related to which compilation passes to reduce unused passes. Experiments with three large-scale MLIR compiler projects find that DuoReduce outperforms syntax-aware reducers such as Perses and Vulcan in terms of IR code reduction by 31.6% and 21.5% respectively. If one uses these reducers by enumerating all possible compilation passes (on average 18 passes), it could take up to 145 hours. By identifying ordering dependencies among compilation passes, DuoReduce reduces this time to 9.5 minutes. By identifying which compilation passes are unused for compiling reduced IR code, DuoReduce reduces the number of passes by 14.6%. This translates to not needing to examine 281 lines of MLIR compiler code on average to fix the bugs. DuoReduce has the potential to significantly reduce debugging effort in MLIR compilers, which serves as the foundation for the current landscape of machine learning and hardware accelerators.more » « less
-
In recent years, the MLIR framework has had explosive growth due to the need for extensible deep learning compilers for hardware accelerators. Such examples include Triton [39], CIRCT [14], and ONNX-MLIR [22]. MLIR compilers introduce significant complexities in localizing bugs or inefficiencies because of their layered optimization and transformation process with compilation passes. While existing delta debugging techniques can be used to identify a minimum subset of IR code that reproduces a given bug symptom, their naive application to MLIR is time-consuming because real-world MLIR compilers usually involve a large number of compilation passes. Compiler developers must identify a minimized set of relevant compilation passes to reduce the footprint of MLIR compiler code to be inspected for a bug fix. We propose DuoReduce, a dual- dimensional reduction approach for MLIR bug localization. DuoReduce leverages three key ideas in tandem to design an efficient MLIR delta debugger. First, DuoReduce reduces compiler passes that are irrelevant to the bug by identifying ordering dependencies among the different compilation passes. Second, DuoReduce uses MLIR-semantics-aware transformations to expedite IR code reduction. Finally, DuoReduce leverages cross-dependence between the IR code dimension and the compilation pass dimension by accounting for which IR code segments are related to which compilation passes to reduce unused passes. Experiments with three large-scale MLIR compiler projects find that DuoReduce outperforms syntax-aware reducers such as Perses and Vulcan in terms of IR code reduction by 31.6% and 21.5% respectively. If one uses these reducers by enumerating all possible compilation passes (on average 18 passes), it could take up to 145 hours. By identifying ordering dependencies among compilation passes, DuoReduce reduces this time to 9.5 minutes. By identifying which compilation passes are unused for compiling reduced IR code, DuoReduce reduces the number of passes by 14.6%. This translates to not needing to examine 281 lines of MLIR compiler code on average to fix the bugs. DuoReduce has the potential to significantly reduce debugging effort in MLIR compilers, which serves as the foundation for the current landscape of machine learning and hardware accelerators.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3387904.3389280
