Measuring the principal of defect debt

Akbarinasaji, Shirin; Bener, Ayşe; Erdem, Atakan

doi:10.1145/2896995.2896999

Cited by 13 publications

(11 citation statements)

References 31 publications

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“…This study clearly shows that it is viable to recommend whether the bug will be fixed quickly or slowly based on the reporter. Studies have shown that the reporter has a positive influence on predicting bug-fixing time [1,2,4,32,[46][47][48]. The collaborative filtering approach can therefore recommend the bug-fixing time based on the reporter, similar to what a recommender system does for the user of a system.…”

Section: Resultsmentioning

confidence: 99%

Predicting Bug-Fixing Time with Machine Learning - A Collaborative Filtering Approach

Rodrigues

Parreiras

2022

Anais Do XIII Computer on the Beach - COTB'22

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Predicting bug-fixing time helps software managers and teams prioritizetasks, allocations and costs in software projects. In literature,machine learning (ML) models have been proposed to predict bugfixingtime. One of features highlighted by studies is the reporter(the person who open the bug) has positive influence in the timeto resolve a bug. In this way, this paper answers the following researchquestion: How does a collaborative filtering approach performin predicting bug-fixing time compared to the supervised machinelearning approaches? In order to answer this question we performedan experiment using collaborative filtering approach to recommendthe bugs that are fast to be resolved in two open software projects.We compare our proposed approach with the ML approach relatedto the literature. As a result, the collaborative filtering approachoutperforms the supervised ML achieving an F-measure of 74%while the supervised ML achieved 66%. The collaborative filteringapproach showed to be a new perspective to predict bug-fixing timein software projects focusing the prediction on the reporter.

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Section: Resultsmentioning

confidence: 99%

Predicting Bug-Fixing Time with Machine Learning - A Collaborative Filtering Approach

Rodrigues

Parreiras

2022

Anais Do XIII Computer on the Beach - COTB'22

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“…Their features include submitter, owner and component fixing time, the severity, and priority level. They conducted an empirical study both on commercial and open source projects and showed that their model outperformed the linear regression model with R 2 ranges between 74% to 85% [11].…”

Section: Prediction Of Blocking Bugmentioning

confidence: 99%

Partially observable Markov Decision Process to prioritize software defects

Akbarinasaji¹

2021

Preprint

Self Cite

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Background: Bug tracking systems receive many bug reports daily. Although the software quality team aims to identify and resolve these bugs, they are never able to fix all of the reported bugs in the issue tracking system before the release deadline. However, postponing the bug fixing may have some consequences. Prioritization of bug reports will help the software manager decide which bugs to fix and which bugs to postpone. Typically, bug reports are prioritized based on the severity, priority, time and effort for fixing, customer pressure, etc. Aim: Previous studies have shown that these factors may not be appropriate for prioritization. Therefore, relying on them to automate bug prioritization might be misleading. In this dissertation, we aim to prioritize bug reports with respect to the consequence of not fixing the bugs in terms of their relative importance in the issue tracking system. Method: In order to measure the relative importance of bugs in the issue tracking system, we propose the construction of a dependency graph based on the reported dependency-blocking information in the issue tracking system. Two metrics, namely depth and degree, are used to measure the relative importance of the bugs. However, there is uncertainty in the dependency graph structure as the dependency information is discovered manually and gradually. Owing to this uncertainty, prioritization of bugs in the descending order of depth and degree may be misleading. To handle the uncertainty, we propose a novel approach of a partially observable Markov decision process (POMDP) and partially observable Monte Carlo planning (POMCP). Result: To check the feasibility of the proposed approach, we analyzed seven years of data from an open source project, Firefox, and a commercial project. We compared the proposed policy with the developer policy, maximum policy, and random policy. Conclusion: The results suggest that software practitioners do not consider the relative importance of bugs in their current practice. The proposed framework can be combined with practitioners’ expertise to prioritize bugs more effectively and take the depth and degree of bugs into account. In practice, the POMDP framework with the POMCP planner can help practitioners sequentially select bugs to minimize the connectivity of the dependency graph.

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“…Estimating technical debt principal was studied by many researchers [16]. The authors in [3] defined the principal as the time needed to fix debt prone bugs, and they used KNN-regression model to predict that time from historical data. Curtis et al [11] presented a formula to calculate the principal of code and architectural level debt.…”

Section: Related Workmentioning

confidence: 99%

Identifying Technical Debt in Database Normalization Using Association Rule Mining

Albarak

Al-Razgan

Bahsoon

2018

2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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Technical debt is a metaphor that describes the long-term effects of shortcuts taken in software development activities to achieve near-term goals. In this study, we explore a new context of technical debt that relates to database normalization design decisions. We posit that ill-normalized databases can have longterm ramifications on data quality and maintainability costs over time, just like debts accumulate interest. Conversely, conventional database approaches would suggest normalizing weakly normalized tables; this can be a costly process in terms of effort and expertise it requires for large software systems. As studies have shown that the fourth normal form is often regarded as the "ideal" form in database design, we claim that database normalization debts are likely to be incurred for tables below this form. We refer to normalization debt item as any table in the database below the fourth normal form.We propose a framework for identifying normalization debt. Our framework makes use of association rule mining to discover functional dependencies between attributes in a table, which will help determine the current normal form of that table and identify debt tables. To manage such debts, we propose a trade-off analysis method to prioritize tables that are candidate for normalization. The trade-off is between the rework cost and the debt effect on the data quality and maintainability as the metaphoric interest. To evaluate our method, we use a case study from Microsoft, AdventureWorks. The results show that our method can reduce the cost and effort of normalization, while improving the database design. CCS CONCEPTS• Software and its engineering → Designing software;• Information systems → Relational Database Model;

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Measuring the principal of defect debt

Cited by 13 publications

References 31 publications

Predicting Bug-Fixing Time with Machine Learning - A Collaborative Filtering Approach

Predicting Bug-Fixing Time with Machine Learning - A Collaborative Filtering Approach

Partially observable Markov Decision Process to prioritize software defects

Identifying Technical Debt in Database Normalization Using Association Rule Mining

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