Learning to rank faulty source files for dependent bug reports

Safdari, Nasir; Alrubaye, Hussein; Aljedaani, Wajdi; Baez, Bladimir Baez; DiStasi, Andrew; Mkaouer, Mohamed Wiem

doi:10.1117/12.2519226

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…• Support Vector Classifier (SVC): It is a popular ML classifier for solving linear and non-linear problems. It works well for several practical applications [318,264].…”

Section: Step 5: Model Selectionmentioning

confidence: 96%

“…Many researchers concluded that reviews and ratings posted by users on app store platforms could play an essential role in apps' evolution since most developers consider users' reviews when working on a new release [91,181,238,245,23,264,31,229]. Maalej et al [192] proposed to consider user input as the first means of requirements elicitation in software development.…”

Section: User Reviewsmentioning

confidence: 99%

“…• Support Vector Classification (SVC): is a well-known machine learning classifier for tackling linear & non-linear issues. It's suitable for a variety of practical applications [264]. SVC generates a hyperplane or line that splits the data in the section.…”

Section: Synthetic Minority Over-sampling Technique (Smote)mentioning

confidence: 99%

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Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

Aljedaani

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The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students find that learning is extremely stressful during the pandemic.

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“…• Support Vector Classifier (SVC): It is a popular ML classifier for solving linear and non-linear problems. It works well for several practical applications [318,264].…”

Section: Step 5: Model Selectionmentioning

confidence: 96%

Section: User Reviewsmentioning

confidence: 99%

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Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

Aljedaani

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“…According to the number of articles for each task, ML techniques are more commonly used for Bug Localization than for Traceability Link Recovery or for Feature Location. While 47% of the articles apply ML techniques for Bug [14], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59] [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85] [10], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97],…”

Section: Related Workmentioning

confidence: 99%

“…In fact, among the retrieved articles, there are only three articles that use Learning to Rank algorithms [74,84,85]. These three articles apply Learning to Rank algorithms for Bug Localization on source code.…”

Section: Related Work Regarding the ML Techniquesmentioning

confidence: 99%

Design of a Machine Learning-based Approach for Fragment Retrieval on Models

Terraza¹,

Cristina²

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In this work, we propose an evolutionary ontological encoding approach to enable Machine Learning techniques to be used to perform Software Engineering tasks in models. The approach is based on a domain ontology to encode a model and on an Evolutionary Algorithm to optimize the encoding. As a result, the encoded model that is returned by the approach can then be used by Machine Learning techniques to perform Software Engineering tasks such as concept location, traceability link retrieval, reuse, impact analysis, etc. We have evaluated the approach with an industrial case study to recover the traceability link between the requirements and the models through a Machine Learning technique (RankBoost). Our results in terms of recall, precision, and the combination of both (F-measure) show that our approach outperforms the baseline (Latent Semantic Indexing). We also performed a statistical analysis to assess the magnitude of the improvement.

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