Improving software maintenance with improved bug triaging

Gupta, Chetan; Inácio, Pedro R. M.; Freire, Mário M.

doi:10.1016/j.jksuci.2021.10.011

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…Their method combines the naive Bayesian and expectation-maximization approaches. Gupta, Inácio & Freire (2021) , along with Jahanshahi et al (2021) , Jahanshahi & Cevik (2022) , and Gupta & Freire (2021) , are among a few researchers who have proposed approaches taking into account the developer’s involvement while allocating bugs. An auction-based blockchain framework is proposed by Gupta & Freire (2021) .…”

Section: Related Workmentioning

confidence: 99%

Enhancing bug allocation in software development: a multi-criteria approach using fuzzy logic and evolutionary algorithms

Gupta,

Gupta

2024

PeerJ Computer Science

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A bug tracking system (BTS) is a comprehensive data source for data-driven decision-making. Its various bug attributes can identify a BTS with ease. It results in unlabeled, fuzzy, and noisy bug reporting because some of these parameters, including severity and priority, are subjective and are instead chosen by the user’s or developer’s intuition rather than by adhering to a formal framework. This article proposes a hybrid, multi-criteria fuzzy-based, and multi-objective evolutionary algorithm to automate the bug management approach. The proposed approach, in a novel way, addresses the trade-offs of supporting multi-criteria decision-making to (a) gather decisive and explicit knowledge about bug reports, the developer’s current workload and bug priority, (b) build metrics for computing the developer’s capability score using expertise, performance, and availability (c) build metrics for relative bug importance score. Results of the experiment on five open-source projects (Mozilla, Eclipse, Net Beans, Jira, and Free desktop) demonstrate that with the proposed approach, roughly 20% of improvement can be achieved over existing approaches with the harmonic mean of precision, recall, f-measure, and accuracy of 92.05%, 89.04%, 90.05%, and 91.25%, respectively. The maximization of the throughput of the bug can be achieved effectively with the lowest cost when the number of developers or the number of bugs changes. The proposed solution addresses the following three goals: (i) improve triage accuracy for bug reports, (ii) differentiate between active and inactive developers, and (iii) identify the availability of developers according to their current workload.

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Section: Related Workmentioning

confidence: 99%

Enhancing bug allocation in software development: a multi-criteria approach using fuzzy logic and evolutionary algorithms

Gupta,

Gupta

2024

PeerJ Computer Science

Self Cite

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“…The scope extends to the exploration of state-of-the-art machine learning models, natural language processing algorithms, and their integration into bug triage systems to optimize bug resolution and enhance overall software quality. Through a detailed examination of current practices and emerging trends, this paper seeks to contribute valuable perspectives and guidelines for practitioners and researchers in the domain of automated bug triage [6] [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Automating Bug Triage: Unleashing the Power of Machine Learning and Natural Language Processing

Kumari,

Ahmed,

Raghuwanshi

et al. 2024

Preprint

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This paper explores the potential of automating bug triage using machine learning and natural language processing. In the context of growing software complexity, manual bug handling faces challenges leading to delays. The study critically examines traditional bug triage methods, emphasizing their limitations and the need for automated solutions. By reviewing various machine learning techniques applied in bug triage, the paper provides insights for practitioners. The proposed Bug Triage model, integrating Word2Vec embeddings and XGBoost, shows promising results in categorizing and prioritizing bug reports. The paper concludes by highlighting future research opportunities to advance bug triage methodologies and improve software development practices.

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“…Sometimes, software maintenance is important to adapt software products to a modified workplace (i.e., environment). Bug triaging is a significant, tedious, and timeconsuming task of SM activity [15].…”

Section: Introductionmentioning

confidence: 99%

BushraDBR: An Automatic Approach to Retrieving Duplicate Bug Reports

Al-Msie’deen

2024

IJCDS

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A Bug Tracking System (BTS), such as Bugzilla, is generally utilized to track submitted Bug Reports (BRs) for a particular software system. Duplicate Bug Report (DBR) retrieval is the process of obtaining a DBR in the BTS. This process is important to avoid needless work from engineers on DBRs. To prevent wasting engineer resources, such as effort and time, on previously submitted (or duplicate) BRs, it is essential to find and retrieve DBRs as soon as they are submitted by software users. Thus, this paper proposes an automatic approach (called BushraDBR) that aims to assist an engineer (called a triager) to retrieve DBRs and stop the duplicates before they start. Where BushraDBR stands for Bushra Duplicate Bug Reports retrieval process. Therefore, when a new BR is sent to the Bug Repository (BRE), an engineer checks whether it is a duplicate of an existing BR in BRE or not via BushraDBR approach. If it is, the engineer marks it as DBR, and the BR is excluded from consideration for any additional work; otherwise, the BR is added to the BRE. BushraDBR approach relies on Textual Similarity (TS) between the newly submitted BR and the rest of the BRs in BRE to retrieve DBRs. BushraDBR exploits unstructured data from BRs to apply Information Retrieval (IR) methods in an efficient way. BushraDBR approach uses two techniques to retrieve DBRs: Latent Semantic Indexing (LSI) and Formal Concept Analysis (FCA). The originality of BushraDBR is to stop DBRs before they occur by comparing the newly reported BR with the rest of the BRs in the BTS, thus saving time and effort during the Software Maintenance (SM) process. BushraDBR also uniquely retrieves DBR through the use of LSI and FCA techniques. BushraDBR approach had been validated and evaluated on several publicly available data sets from Bugzilla. Experiments show the ability of BushraDBR approach to retrieve DBRs in an efficient and accurate manner.

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Improving software maintenance with improved bug triaging

Cited by 7 publications

References 38 publications

Enhancing bug allocation in software development: a multi-criteria approach using fuzzy logic and evolutionary algorithms

Enhancing bug allocation in software development: a multi-criteria approach using fuzzy logic and evolutionary algorithms

Automating Bug Triage: Unleashing the Power of Machine Learning and Natural Language Processing

BushraDBR: An Automatic Approach to Retrieving Duplicate Bug Reports

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