IR-based Bug Localization Research Articles

An Enhanced Information Retrieval-Based Bug Localization System with Code Coverage, Stack Traces, and Spectrum Information

Several strategies such as Vector Space Model (VSM), revised Vector Space Model (rVSM), and integration of additional elements such as stack trace and previously corrected bug report have been utilized to improve the Information Retrieval (IR) based bug localization process. Most of the existing IR-based approaches make use of source code files without filtering, which eventually increases the search space of the technique, thereby slowing down the bug localization process. This study developed an enhanced IR-based bug localization model as a viable solution. Specifically, an enhanced rVSM (e-rVSM) is developed based on the hybridization of code coverage, stack traces, and spectrum information. Combining the stack trace and spectrum information as additional features can enhance the accuracy of the IR-based technique by boosting the bug localization process. Code coverage analysis was conducted to remove irrelevant source files and reduce the search space of the IR technique. Then the filtered source files are preprocessed via tokenization and stemming from selecting relevant features and removing unwanted words. The preprocessed data is further analyzed by finding similarities between the preprocessed bug reports and source code files using the e-rVSM. Finally, scores for each source code and suspected buggy files are ranked in descending order. The performance of the proposed e-rVSM is tested on two open-source projects (Zxing and SWT), and its effectiveness is assessed using TopN rank (where N = 5, 10), Mean Reciprocal Rank (MRR), and Mean Average Precision (MAP). Findings from the experimental results revealed the effectiveness of e-rVSM in bug localization. In particular, e-rVSM recorded a significant Top 5 (80.2%; 65%) and Top 10 (89.1%; 75%) rank values on SWT and Zxing dataset respectively. Also, the proposed e-rVSM had MRR values of 80% and 54% on the SWT dataset and MAP values of 61.22% and 47.23% on the Zxing dataset.

An empirical study of the effectiveness of IR-based bug localization for large-scale industrial projects

An empirical study of the effectiveness of IR-based bug localization for large-scale industrial projects

The forgotten role of search queries in IR-based bug localization: an empirical study

Being light-weight and cost-effective, IR-based approaches for bug localization have shown promise in finding software bugs. However, the accuracy of these approaches heavily depends on their used bug reports. A significant number of bug reports contain only plain natural language texts. According to existing studies, IR-based approaches cannot perform well when they use these bug reports as search queries. On the other hand, there is a piece of recent evidence that suggests that even these natural language-only reports contain enough good keywords that could help localize the bugs successfully. On one hand, these findings suggest that natural language-only bug reports might be a sufficient source for good query keywords. On the other hand, they cast serious doubt on the query selection practices in the IR-based bug localization. In this article, we attempted to clear the sky on this aspect by conducting an in-depth empirical study that critically examines the state-of-the-art query selection practices in IR-based bug localization. In particular, we use a dataset of 2,320 bug reports, employ ten existing approaches from the literature, exploit the Genetic Algorithm-based approach to construct optimal, near-optimal search queries from these bug reports, and then answer three research questions. We confirmed that the state-of-the-art query construction approaches are indeed not sufficient for constructing appropriate queries (for bug localization) from certain natural language-only bug reports. However, these bug reports indeed contain high-quality search keywords in their texts even though they might not contain explicit hints for localizing bugs (e.g., stack traces). We also demonstrate that optimal queries and non-optimal queries chosen from bug report texts are significantly different in terms of several keyword characteristics (e.g., frequency, entropy, position, part of speech). Such an analysis has led us to four actionable insights on how to choose appropriate keywords from a bug report. Furthermore, we demonstrate 27%–34% improvement in the performance of non-optimal queries through the application of our actionable insights to them. Finally, we summarize our study findings with future research directions (e.g., machine intelligence in keyword selection).

INFORMATION RETRIEVAL BUG LOCALIZATION WITH WAVELETS ANALYSIS

Nowadays, maintaining an oversized and evolving computer code involves longer and value for the project team. In software maintenance, a bug report is employed to seek out a fault location. Once a bug report is received, it is suiTable.to automatically denote out the files that developers should change to repair the bug. This work develops a brand-new information retrieval (IR) system that allows representing textual data by signals. This new way of implementing gives us the chance to use various mathematical tools from the signal theory like Wavelets Transforms, unused nowadays within the field of IR. This paper proposes Wavelets Transforms for Bug Localization (WTBugLoc), a mathematical approach of IR-based bug localization using wavelet techniques. The results of the conducted experiments on the SWT (Standard Widget Toolkit) Eclipse project confirm the effectiveness of the proposed approach. The experiments also show that WTBugLoc outperforms method using the Vector Space Model (VSM).

An extensive study on smell-aware bug localization

Bug localization is an important aspect of software maintenance because it can locate modules that should be changed to fix a specific bug. Our previous study showed that the accuracy of the information retrieval (IR)-based bug localization technique improved when used in combination with code smell information. Although this technique showed promise, the study showed limited usefulness because of the small number of: (1) projects in the dataset, (2) types of smell information, and (3) baseline bug localization techniques used for assessment. This paper presents an extension of our previous experiments on Bench4BL, the largest bug localization benchmark dataset available for bug localization. In addition, we generalized the smell-aware bug localization technique to allow different configurations of smell information, which were combined with various bug localization techniques. Our results confirmed that our technique can improve the performance of IR-based bug localization techniques for the class level even when large datasets are processed. Furthermore, because of the optimized configuration of the smell information, our technique can enhance the performance of most state-of-the-art bug localization techniques.

ManQ: Many-objective optimization-based automatic query reduction for IR-based bug localization

Abstract Context An information retrieval-based bug localization (IRBL) method is proposed to localize buggy files using a bug report as a query. The performance of this method strongly depends on the quality of the query. However, these queries contain noise terms that hinder their use for IRBL. To improve the quality of a query, an automatic query reduction (AQR) technique that removes noise words from the query is needed. Objective Our objective is to develop an AQR method for IRBL. Most existing AQR techniques are based on single objective optimization, which presents issues in terms of biased and limited performance. To solve these issues, it is necessary to find a subquery that comprehensively satisfies all of their objectives. Method We propose an AQR technique called ManQ, which is a many-objective optimization-based AQR method for IRBL. We design 15 objective functions to (1) maintain the query quality properties, (2) maintain the important terms, (3) maintain the initial information, and (4) minimize the query length. ManQ finds a final subquery that maximize the return values of these objective functions. Results The experimental results show that ManQ improves the quality of poor queries. We also show that if we select the best query among the candidates generated by ManQ, we can increase the number of improved queries by more than 53.4% of all queries. Conclusion ManQ improves the performance of IRBL by improving the quality of queries through a many-objective optimization approach.

Structured information in bug report descriptions—influence on IR-based bug localization and developers

Multiple information retrieval (IR)-based bug localization techniques have been proposed over the last years. The foundation of the approaches relies on textual similarity of the bug report description and the source code files. The basic assumption is that these descriptions are well suited to query the code base. However, often bug reports contain structured information such as stack traces and source code next to natural language, which might interfere with the initial belief. In this paper, we systematically analyze the influence of structured information on IR-based techniques. Therefore, an empirical study on 7334 bug reports, out of which more than 30% contain structured information, was carried out. Based on the results, a follow-up user study was conducted focusing on source code fragments found in bug reports. Our results show that stack traces tend to negatively affect IR-based bug localization performance and require special handling. Compared to natural language–only reports, source code is beneficial for IR-based algorithms, as well as for developers to identify false positives in bug localization results.

Bug Localization for Version Issues With Defect Patterns

Version issues are becoming more and more prominent with the continuous development of software. Bug localization for version issues is time-consuming and labor-intensive. Although some bug localization techniques, such as those based on information retrieval (IR), have been proposed, they cannot handle these bugs very well as the version-related bugs have their own defect patterns. However, few existing works have focused on revealing these defect patterns and utilizing them for localization of bugs. To fill this gap, we propose a new approach by leveraging the version-related defect patterns to localize the version-related issues integrated with the IR technique. First, we extract version-related bugs from bug repositories and build a version-related bug repository. Given a new version-related bug report, we identify the defect patterns of corresponding similar historical bug reports from the version-related bug repository. Then, we combine these defect patterns with the IR technique to rank the candidate code snippets as suspicious code for developers to fix. The evaluation demonstrates that our approach is more effective to identify the faulty code related to version issues than the existing IR-based bug localization technique.

Comparing learning to rank techniques in hybrid bug localization

Bug localization is a software development and maintenance activity that aims to find relevant source code entities to be modified so that a specific bug can be fixed on the basis of the given bug report. Information retrieval (IR) techniques have been widely used to locate bugs in recent decades. These techniques mainly use the IR similarity between the bug report and source code entities. In addition to IR similarity, features that are extracted from version history, source code structure, dynamic analysis, and other resources are found to be beneficial for bug localization. The approaches utilizing extra features in IR-based bug localization are called hybrid bug localization. We conduct a short survey of the hybrid bug localization methods that use additional features in addition to IR similarity. We also use Learning to Rank (LtR) techniques to combine the beneficial features to improve bug localization. Learning to Rank is the application of machine learning in the ranking models for information retrieval. We compared eight LtR techniques in bug localization, and the experimental results show that coordinate ascent algorithms without normalization is a suitable LtR technique in bug localization for selected attributes, and it outperforms two state-of-the-art localization approaches for two large projects, Eclipse and SWT.

Will this localization tool be effective for this bug? Mitigating the impact of unreliability of information retrieval based bug localization tools

Information retrieval (IR) based bug localization approaches process a textual bug report and a collection of source code files to find buggy files. They output a ranked list of files sorted by their likelihood to contain the bug. Recently, several IR-based bug localization tools have been proposed. However, there are no perfect tools that can successfully localize faults within a few number of most suspicious program elements for every single input bug report. Therefore, it is difficult for developers to decide which tool would be effective for a given bug report. Furthermore, for some bug reports, no bug localization tools would be useful. Even a state-of-the-art bug localization tool outputs many ranked lists where buggy files appear very low in the lists. This potentially causes developers to distrust bug localization tools. In this work, we build an oracle that can automatically predict whether a ranked list produced by an IR-based bug localization tool is likely to be effective or not. We consider a ranked list to be effective if a buggy file appears in the top-N position of the list. If a ranked list is unlikely to be effective, developers do not need to waste time in checking the recommended files one by one. In such cases, it is better for developers to use traditional debugging methods or request for further information to localize bugs. To build this oracle, our approach extracts features that can be divided into four categories: score features, textual features, topic model features, and metadata features. We build a separate prediction model for each category, and combine them to create a composite prediction model which is used as the oracle. We name this solution APRILE, which stands for Automated PRediction of IR-based Bug Localization's Effectiveness. We further integrate APRILE with two other components that are learned using our bagging-based ensemble classification (BEC) method. We refer to the extension of APRILE as APRILE +. We have evaluated APRILE + to predict the effectiveness of three state-of-the-art IR-based bug localization tools on more than three thousands bug reports from AspectJ, Eclipse, SWT, and Tomcat. APRILE + can achieve an average precision, recall, and F-measure of 77.61 %, 88.94 %, and 82.09 %, respectively. Furthermore, APRILE + outperforms a baseline approach by Le and Lo and APRILE by up to a 17.43 % and 10.51 % increase in F-measure respectively.