International Research Journal of Modernization in Engineering Technology and Science · 2025
Software quality assurance is a vital aspect of software development, where predicting bugs in code modules before deployment can significantly reduce maintenance costs and improve reliability.This project, Software Bug Prediction, presents a machine learning-based web application built using Flask to predict the likelihood of bugs in software files using static code metrics and project metadata.The backend system integrates two effective classification algorithms-Random Forest Classifier and Logistic Regression-to identify the presence of bugs.It considers features such as module type, programming language, lines of code, cyclomatic complexity, developer experience, code review scores, and test coverage.Categorical features are label encoded, and all features are standardized to enhance model performance.The application trains both models and evaluates them on a hold-out test set, reporting metrics such as accuracy and feature importance.Through a web interface, users can upload or use a dataset, initiate model training, and enter input feature values for prediction.The system outputs the probability of bugs from both models and gives a final result based on an average probability threshold.Additionally, it offers insights like bug rate and feature distribution.This scalable system supports automated bug risk assessment and improves decision-making in software development.
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SUN CHENGNIAN · National University of Singapore · 2013
Ph.D
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Alina Nazmeeva · 2024
This article discusses the conceptual underpinnings, creative process, and research methods used to create the immersive installation titled bug in my software. bug in my software reflects on the tensions between gender, labor, and technology by drawing parallels from the engineered lifecycle and genetically modified DNA of a silkmoth to the womxn-dominated textile labor industry and gendered biases embedded in AI development. The project combines textile installation, extended reality, and a screen-based speculative ecosystem simulation. Here, the silkmoth—both organic and an engineered creature — serves as a proxy to examine the relationship between gender, labor, human and non-human intelligence.
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Cheng Zhou · 2018
Software bugs continuously emerge during the process of software evolution. With the increasing size and complexity of software, bug fixing becomes increasingly more difficult. Bug and commit data of open source projects, Q&A documents and other software resources contain a sea of bug knowledge which can be utilized to help developers understand and fix bugs. Existing work focuses on data mining from a certain software resource in isolation to assist in bug fixing, which may reduce the efficiency of bug fixing.
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Yuan Tian · Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University) · 2017
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Naresh Kumar Nagwani, Shekhar Verma · IGI Global eBooks · 2017
Software repositories contain a wealth of information that can be analyzed for knowledge extraction. Software bug repositories are one such repository that stores the information about the defects identified during the development of software. Information available in software bug repositories like number of bugs priority-wise, component-wise, status-wise, developers-wise, module-wise, summary-terms-wise, can be visualized with the help of two- or three-dimensional graphs. These visualizations help in understanding the bug distribution patterns, software matrices related to the software bugs, and developer information in the bug-fixing process. Visualization techniques are exploited with the help of open source technologies in this chapter to visualize the bug distribution information available in the software bug repositories. Two-dimensional and three-dimensional graphs are generated using java-based open source APIs, namely Jzy3d (Java Easy 3d) and JFreeChart. Android software bug repository is selected for the experimental demonstrations of graphs. The textual bug attribute information is also visualized using frequencies of frequent terms present in it.
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William Gould · CHANCE · 1995
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Cristiano Werner Araújo · Americanae (AECID Library) · 2017
Informação relacionada a concertos de bugs tem sido explorada na construção de preditores de bugs cuja função é o suporte para a verificação de sistemas de software identificando quais elementos, como arquivos, são mais propensos a bugs. Uma grande variedade de métricas estáticas de código e métricas de mudança já foi utilizada para construir tais preditores. Dos muitos preditores de bugs propostos, a grande maioria foca em sistemas orientados à objeto. Apesar de orientação a objetos ser o paradigma de escolha para a maioria das aplicações, o paradigma procedural ainda é usado em várias — muitas vezes cruciais — aplicações, como sistemas operacionais e sistemas embarcados. Portanto, eles também merecem atenção. Essa dissertação extende o trabalho na área de predição de bugs ao avaliar e aprimorar preditores de bugs para sistemas procedurais de software. Nós proporcionamos três principais contribuições: (i) comparação das abordagens existentes de predição de bugs no contexto de sistemas procedurais, (ii) proposta de uso dos atributos de qualidade de software como atributos de predição no contexto estudado e (iii) avaliação dos atributos propostos em conjunto com a melhor abordagem encontrada em (i). Nosso trabalho provê, portanto, fundamentos para melhorar a performance de preditores de bugs no contexto de sistemas procedurais.
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Le An · PolyPublie (École Polytechnique de Montréal) · 2015
RÉSUMÉ: La correction de bogues est une activité majeure pendant le développement et maintenance de logiciels. Durant cette activité, le tri de bogues joue un rôle essentiel. Il aide les gestionnaires à allouer leurs ressources limitées et permet aux développeurs de concentrer leurs efforts plus efficacement sur les bogues à haute sévérité. Malheureusement, les techniques du tri de bogues appliquées dans beaucoup d'entreprises ne sont pas toujours efficaces et conduisent à la misclassifications de bogues ou à des retards dans leurs résolutions, qui peuvent mener à la dégradation de la qualité d'un logiciel et à la déception de ses utilisateurs. Une stratégie de tri de bogues améliorée est nécessaire pour aider les gestionnaires à prendre de meilleures décisions, par exemple en accordant des degrés de priorité et sévérité appropriés aux bogues, ce qui permet aux développeurs de corriger les problèmes critiques le plus tôt possible en ignorant les problèmes futiles. Dans ce mémoire, nous utilisons les approches analytiques pour améliorer le tri de bogues. Nous réalisons trois études empiriques. La première étude porte sur la relation entre les corrections de bogues qui ont besoin d'autres corrections ultérieures (corrections supplémentaires) et les bogues qui ont été ouverts plus d'une fois (bogues ré-ouverts). Nous observons que les bogues ré-ouverts occupent entre 21,6% et 33,8% de toutes les corrections supplémentaires. Un grand nombre de bogues ré-ouverts (de 33,0% à 57,5%) n'ont qu'une correction préalable : les bogues originaux ont été fermés prématurément. La deuxième étude concerne les bogues qui provoquent des plantages fréquents, affectant de nombreux utilisateurs. Nous avons observé que ces bogues ne reçoivent pas toujours une attention adéquate même s'ils peuvent sérieusement dégrader la qualité d'un logiciel et même la réputation de l'entreprise. Notre troisième étude concerne les commits qui conduisent à des plantages. Nous avons trouvé que ces commits sont souvent validés par des développeurs moins expérimentés et qu'ils contiennent plus d'additions et de suppressions de lignes de code que les autre commits. Si les entreprises de logiciels pourraient détecter les problèmes susmentionnés pendant la phase du tri de bogues, elles pourraient augmenter l'efficacité de leur correction de bogues et la satisfaction de leurs utilisateurs, réduisant le coût de la maintenance de logiciels. En utilisant plusieurs algorithmes de régression et d'apprentissage automatique, nous avons bâti des modèles statistiques permettant de prédire respectivement des bogues ré-ouverts (avec une précision atteignant 97,0% et un rappel atteignant 65,3%), des bogues affectant un grand nombre d'utilisateurs (avec une précision atteignant 64,2% et un rappel atteignant 98.3%) et des commits induisant des plantages (avec une précision atteignant 61,4% et un rappel atteignant 95,0%). Les entreprises de logiciels peuvent appliquer nos modèles afin d'améliorer leur stratégie de tri de bogues, éviter les misclassifications de bogues et réduire la insatisfaction des utilisateurs due aux plantages. ABSTRACT: Bug fixing has become a major activity in software development and maintenance. In this process, bug triaging plays an important role. It assists software managers in the allocation of their limited resources and allow developers to focus their efforts more efficiently to solve defects with high severity. Current bug triaging techniques applied in many software organisations may lead to misclassification of bugs, thus delay in bug resolution; resulting in degradation of software quality and users' frustration. An improved bug triaging strategy would help software managers make better decisions by assigning the right priority and severity to bugs, allowing developers to address critical bugs as soon as possible and ignore the trivial ones. In this thesis, we leverage analytic approaches to conduct three empirical studies aimed at improving bug triaging techniques. The first study investigates the relation between bug fixes that need supplementary fixes and bugs that have been re-opened. We found that re-opened bugs account from 21.6% to 33.8% of all supplementary bug fixes. A considerable number of re-opened bugs (from 33.0% to 57.5%) had only one commit associated: their original bug reports were prematurely closed. The second study focuses on bugs that yield frequent crashes and impact large numbers of users. We found that these bugs were not prioritised by software managers albeit they can seriously decrease user-perceived quality and even the reputation of a software organisation. Our third study examines commits that lead to crashes. We found that these commits are often submitted by less experienced developers and that they contain more addition and deletion of lines of code than other commits. If software organisations can detect the aforementioned problems early on in the bug triaging phase, they can effectively increase their development productivity and users' satisfaction, while decreasing software maintenance overhead. By using multiple regression and machine learning algorithms, we built statistical models to predict re-opened bugs among bugs that required supplementary bug fixes (with a precision up to 97.0% and a recall up to 65.3%), bugs with high crashing impact (with a precision up to 64.2% and a recall up to 98.3%), and commits inducing future crashes (with a precision up to 61.4% and a recall up to 95.0%). Software organisations can apply our proposed models to improve their bug triaging strategy by assigning bugs to the right developers, avoiding misclassification of bugs, reducing the negative impact of crash-related bugs, and addressing fault-prone code early on before they impact a large user base.
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Scott McPeak, Charles-Henri Gros, Murali Krishna Ramanathan · 2013
An important, but often neglected, goal of static analysis for detecting bugs is the ability to show defects to the programmer quickly. Unfortunately, existing static analysis tools scale very poorly, or are shallow and cannot find complex interprocedural defects. Previous attempts at reducing the analysis time by adding more resources (CPU, memory) or by splitting the analysis into multiple sub-analyses based on defect detection capabilities resulted in limited/negligible improvements.
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Microelectronics Reliability · 1984
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Frank Eichinger, Klemens Böhm · The Kluwer international series on advances in database systems · 2010
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Ahmad Haizar Bin Sahmat, Tan Ping Ping · 2025
Most information technology (IT) companies still rely on manual approaches to manage and track software bugs during testing sessions. This traditional method is time-consuming, error-prone, and lacks efficiency particularly in large or complex software projects. To address these challenges, this paper proposes an artificial intelligence AI-powered Software Bug Tracking Tool designed to streamline the process of reporting, tracking, categorizing, and prioritizing bugs. By leveraging Artificial Intelligence (AI) technologies, including machine learning, the system can automatically classify bugs based on their severity, providing valuable insights for better decision-making. Additionally, the system employs unsupervised learning techniques to detect patterns and relationships among bugs. An OpenAI GPT-4o model, accessed via the Azure OpenAI Service, is integrated into the system to automatically generate summaries of bug reports, helping team quickly understand the key details. A user-friendly design is also provided to support collaboration between developers and testers, reducing the learning curve and improving overall software quality. For bug severity detection, the F1-score is 0.64 when detection Critical bugs; an indicator that the approach might be useful. Similarity bug detection is more challenging because the similarity scores might not necessarily reflect the bug similarity. Test case summarization is based on the OpenAI API. The implementation of this system aims to enhance team productivity, ensure comprehensive bug tracking, and align with modern software development practices. Based on the usability testing with 10 participants, including software expert from various background such as software testers and developers, the duplicate detection and bug summarization features are particularly useful, to streamline the bug tracking process and improve the understanding of reported issues. However, the bug severity prediction feature produced unexpected classifications in certain cases, indicating the need for additional training data and the potential future works to enable the model to continuously improve over time.
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Sweety Ahlawat, Dhiraj Khurana · 2025
In order to prioritize issues according to their relevance, teams that use software defect management are required to grade the severity of defects. This is because old manual classification procedures become more prone to subjectivity, inefficiency, and mistakes as software systems get more complex. This research provides an automated technique for forecasting the severity of defects that is based on convolutional neural networks (CNNs). The purpose of this paper is to solve the difficulties that have been raised. CNNs are able to discover complicated patterns and produce reliable severity estimates by evaluating textual data generated by bug reports. This is accomplished with little feature engineering by CNNs. As a result of using bug reports from the Mozilla and Eclipse projects, the suggested approach is assessed, and it achieves an accuracy of 90.6% and 91.3%, respectively. The accuracy, recall, and F-measure are three additional metrics that can be utilized to evaluate the efficacy of the model. According to the findings of the research, CNNs have the potential to greatly improve bug discovery.
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K. C. Mohanraj, M. Prasanth, S. Rafith et al. · 2024
Software bug prediction is the process of locating and predicting potential issues in a software project by using a range of techniques before they become costly and cause disruptive issues. The 'Deep Learning based Software Bug Tracker' is a novel approach presented in this research study. The primary goal of the proposed system is to revolutionize traditional bug tracking systems by automatically identifying, categorizing, and prioritizing software defects through the use of cutting-edge deep learning algorithms. "Software bug prediction" is the technique used for determining which software components have the highest likelihood of containing problems. To detect software flaws, the proposed system primarily employs two predictive modeling techniques: Learning to Rank (LTR) and Linear Regression (LR). It entails obtaining information from relevant and historical defect sources, preprocessing the information to clean and convert it, selecting features to identify important signals, and teaching Learning to Rank (LTR) and LR models. When put into practice, this method makes use of attributes to calculate the likelihood that software modules would have errors. The system's output controls how resources and testing strategies are used, enhancing the quality of software and development productivity. It is possible to effectively address proactive defect control during software development by using ongoing monitoring and adjustments that provide accuracy over time.
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Marko Okreša · FER Repository · 2022
U ovom radu opisan je razvoj programske podrške za praćenje zahtjeva i pogrešaka u DevOps okruženju. Na početku su odabrana slična programska rješenja i rezultatima njihove analize, opisane su funkcionalnosti koje pružaju. Prethodnim rezultatima analize sličnih programskih rješenja i njihovih funkcionalnosti, specificirali su se zahtjevi koje programska podrška razvijena u sklopu ovog rada mora ispuniti. Nakon toga opisana je arhitektura i model podataka razvijenog sustava. U poglavlju nakon toga opisana je i prikazana pomoću slika sva funkcionalnost razvijene programske podrške. Sljedeće su navedene i opisane sve korištene tehnologe i alati koji su bili korištenu u razvoju programske podrške. Na samom kraju dan je zaključak o rezultatima izrade rada.
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Tomas Holek, Miroslav Bureš, Tomas Cerny · Lecture notes in networks and systems · 2024
Cited by 1Open access
Muhammad Bilal Bashir, Misbah Masood, Abdul Qadir et al. · 2013
World Wide Web (www) is producing immense amount of data, making it difficult for users to manage, analyze, and then bring useful information out of it. A user may not be able to understand the data and concepts in a document produced by a user that belongs to a different domain. Also data produced on heterogeneous systems cannot be integrated with ease. XML (extensible markup language) helps in all these scenarios where users can describe their data and concepts using tags. Data can be shared and integrated among heterogeneous systems using XML. Also intelligent machines can be built to process data from XML documents and analyze them. The Software bug Markup Language (bugML) is an effort to provide XML structure to software bug information. We have extended this work and have proposed some additions to bugML so it can be described comprehensively. We have also implemented a tool that we name as Auto-bugMLX, to automate our work.
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Dhyan ChandraYadav, Saurabh Pal · International Journal of Computer Applications · 2015
The common software problems appear in a wide variety of applications and environments. Some software related problems arises in software project development i.e. software related problems are known as software defect in which Software bug is a major problem arises in the coding implementation .There are no satisfied result found by project development team. The software bug problems mentation in problem report and software engineer does not easily detect this software defect but by the help of data mining classification software engineers easily can classify software bug. This paper classified and detect software bug by J48, ID3 and Nave Bayes data mining algorithms. Comparison of these algorithms to detect accuracy and time taken to build model is also presented in this paper.
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Ahmed Fawzi Otoom, Sara Al-jdaeh, Maen Hammad · 2019
We target the problem of software bug reports classification. Our main aim is to build a classifier that is capable of classifying newly incoming bug reports into two predefined classes: corrective (defect fixing) report and perfective (major maintenance) report. This helps maintainers to quickly understand these bug reports and hence, allocate resources for each category. For this purpose, we propose a distinctive feature set that is based on the occurrences of certain keywords. The proposed feature set is then fed into a number of classification algorithms for building a classification model. The results of the proposed feature set achieved high accuracy in classification with SVM classification algorithm reporting an average accuracy of (93.1%) on three different open source projects.
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