On the use of relevance feedback in IR-based concept location

Haiduc, Sonia; Marcus, Andrian; Menzies, Tim

doi:10.1109/icsm.2009.5306315

Cited by 143 publications

(108 citation statements)

References 27 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…In [8], Gay et al showed that ERF can assist developers in locating the target file(s) by reformulating the original query iteratively. At each iteration, the reformulated query is submitted and a new set of retrieval results is presented to the user to obtain the next round of feedback.…”

Section: A Explicit Relevance Feedbackmentioning

confidence: 99%

“…As it turns out, researchers in the software engineering community have looked at QR in the past -but in a way that either places additional burden on the developer in constructing a query or that are based on general statistical properties of a software library. To elaborate, as an example of QR that places additional burden on the developer, we have the study of Explicit Relevance Feedback (ERF) by Gay et al [8]. They used Rocchio's method for QR to locate a target file by reformulating the original query [9] through an iterative interaction with the user.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assisting code search with automatic Query Reformulation for bug localization

Sisman

Kak

2013

2013 10th Working Conference on Mining Software Repositories (MSR)

View full text Add to dashboard Cite

Abstract-Source code retrieval plays an important role in many software engineering tasks. However, designing a query that can accurately retrieve the relevant software artifacts can be challenging for developers as it requires a certain level of knowledge and experience regarding the code base. This paper demonstrates how the difficulty of designing a proper query can be alleviated through automatic Query Reformulation (QR) -an under-the-hood operation for reformulating a user's query with no additional input from the user. The proposed QR framework works by enriching a user's search query with certain specific additional terms drawn from the highest-ranked artifacts retrieved in response to the initial query. The important point here is that these additional terms injected into a query are those that are deemed to be "close" to the original query terms in the source code on the basis of positional proximity. This similarity metric is based on the notion that terms that deal with the same concepts in source code are usually proximal to one another in the same files. We demonstrate the superiority of our QR framework in relation to the QR frameworks wellknown in the natural language document retrieval by showing significant improvements in bug localization performance for two large software projects using more than 4,000 queries.

show abstract

Section: A Explicit Relevance Feedbackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assisting code search with automatic Query Reformulation for bug localization

Sisman

Kak

2013

2013 10th Working Conference on Mining Software Repositories (MSR)

View full text Add to dashboard Cite

show abstract

“…Henceforth, we refer to this approach as the batch mode approach. However, this can be time consuming and can lead to high query latency 1 . Alternatively, one could re-compute the index and the model at major releases and ignore commit-level changes (henceforth, we refer to this approach as the limited update).…”

Section: Introductionmentioning

confidence: 99%

“…Effort has also gone into studying and improving the quality of the query to improve retrieval accuracy [1] [7][8] [9] [10]. In other words, research in the area of IR based bug localization has focused primarily on retrieval effectiveness.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An incremental update framework for efficient retrieval from software libraries for bug localization

Rao

Medeiros

Kak

2013

2013 20th Working Conference on Reverse Engineering (WCRE)

View full text Add to dashboard Cite

Abstract-Information Retrieval (IR) based bug localization techniques use a bug reports to query a software repository to retrieve relevant source files. These techniques index the source files in the software repository and train a model which is then queried for retrieval purposes. Much of the current research is focused on improving the retrieval effectiveness of these methods. However, little consideration has been given to the efficiency of such approaches for software repositories that are constantly evolving. As the software repository evolves, the index creation and model learning have to be repeated to ensure accuracy of retrieval for each new bug. In doing so, the query latency may be unreasonably high, and also, re-computing the index and the model for files that did not change is computationally redundant. We propose an incremental update framework to continuously update the index and the model using the changes made at each commit. We demonstrate that the same retrieval accuracy can be achieved but with a fraction of the time needed by current approaches. Our results are based on two basic IR modeling techniques -Vector Space Model (VSM) and Smoothed Unigram Model (SUM). The dataset we used in our validation experiments was created by tracking commit history of AspectJ and JodaTime software libraries over a span of 10 years.

show abstract

An empirical study on how expert knowledge affects bug reports

Rodeghero

Huo

Ding

et al. 2016

J Software Evolu Process

View full text Add to dashboard Cite

Bug reports are crucial software artifacts for both software maintenance researchers and practitioners. A typical use of bug reports by researchers is to evaluate automated software maintenance tools: a large repository of reports is used as input for a tool, and metrics are calculated from the tool's output. But this process is quite different from practitioners, who distinguish between reports written by experts, such as programmers, and reports written by non-experts, such as users. Practitioners recognize that the content of a bug report depends on its author's expert knowledge. In this paper, we present an empirical study of the textual difference between bug reports written by experts and non-experts. We find that a significant difference exists and that this difference has a significant impact on the results from a state-of-the-art feature location tool. Through an additional study, we also found no evidence that these encountered differences were caused by the increased usage of terms from the source code in the expert bug reports. Our recommendation is that researchers evaluate maintenance tools using different sets of bug reports for experts and nonexperts.

show abstract

On the use of relevance feedback in IR-based concept location

Abstract: Concept location is a critical activity during

Cited by 143 publications

References 27 publications

Assisting code search with automatic Query Reformulation for bug localization

Assisting code search with automatic Query Reformulation for bug localization

An incremental update framework for efficient retrieval from software libraries for bug localization

An empirical study on how expert knowledge affects bug reports

Contact Info

Product

Resources

About