A noun-based approach to feature location using time-aware term-weighting

Zamani, Sima; Lee, Sai Peck; Shokripour, Ramin; Anvik, John

doi:10.1016/j.infsof.2014.03.007

Cited by 34 publications

(17 citation statements)

References 29 publications

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“…Zamani et al [29] proposed an approach that included weighting and ranking the source code locations based on both the textual similarity with a change request and the use of the time metadata. This approach gives better results than IR techniques.…”

Section: Related Workmentioning

confidence: 99%

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

Arcega

Font

Haugen

et al. 2018

Lecture Notes in Information Systems and Organisation

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Bug location is a common task in Software Engineering, specially when maintaining and evolving software products. When locating bugs in code, results depend greatly on the way code modification timespans are weighted. However, the influence of timespan weightings on bug location in models has not received enough attention yet. Throughout this paper, we analyze the influence of several timespan weightings on bug location in models. These timespan weightings guide an evolutionary algorithm, which returns a ranking of model fragments relevant to the solution of a bug. We evaluated our timespan weightings in BSH, a real-world industrial case study, by measuring the results in terms of recall, precision, and F-measure. Results show that the use of the most recent timespan model modifications provide the best results in our study. We also performed a statistical analysis to provide evidence of the significance of the results.

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Section: Related Workmentioning

confidence: 99%

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

Arcega

Font

Haugen

et al. 2018

Lecture Notes in Information Systems and Organisation

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“…To reduce the time and cost of bug assignment process, the first automatic bug triager was proposed by Cubranic and Murphy [7]. Thereafter, many automatic bug triage approaches were proposed that are based on machine learning [8][9][10][11][12][13][14][15][16], metadata [17][18][19][20][21][22][23], or developer profile [24][25][26][27]. These are shown in Table 1.…”

Section: Bug-repot Triagementioning

confidence: 99%

“…Only Sisman & kak [33] used the time-metadata for feature location approach. Sima, Sai & Ramin [23] has proposed an approach that included weighting and ranking the source-code locations based on both the textual similarity with a change request and the use of the time-metadata. It used only the noun terms for weighting to reduce the dataset volume.…”

Section: Meta-data Basedmentioning

confidence: 99%

Survey Based Classification of Bug Triage Approaches

Yadav¹,

Singh²

2016

csit

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This paper presents a comprehensive survey of bug triaging approaches in three classes namely machine learning based, meta-data based and profile based. All approaches under three categories are critically compared and some potential future directions and challenges are reported. Findings from the survey show that there is a lot of scope to work in cold-start problem, developer- profiling, load balancing, and reopened bug analysis.

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“…Recent studies in software engineering have proved the usefulness of POS-tagging techniques to remove textual noise in software documents [153]. In addition, the use of word-selection strategies [154,155] can improve the results in feature location [156]. After applying this technique, each word is tagged, which allows the removal of some categories that do not provide relevant information.…”

Section: Latent Semantic Analysismentioning

confidence: 99%

Automating the variability formalization of a model family by means of common variability language

Font

Ballarín

Haugen

et al. 2015

Proceedings of the 19th International Conference on Software Product Line

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INTRODUCTION Research Questions related to these challenges: Research Question 1: How to identify and formalize the variability present among a set of product models in terms of features realized by model fragments? Research Question 2: How to capitalize on expert domain knowledge to boost the process of feature location? 1.7. Structure of the dissertation 1.7 Structure of the dissertation This dissertation is structured into five parts: Part I The first part is the introduction of the dissertation, later it presents some background and discusses the state of the art.

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A noun-based approach to feature location using time-aware term-weighting

Cited by 34 publications

References 29 publications

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

Survey Based Classification of Bug Triage Approaches

Automating the variability formalization of a model family by means of common variability language

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