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Empirical Software Engineering
Erschienen in: Empirical Software Engineering 4/2022

01.07.2022

Revisiting reopened bugs in open source software systems

verfasst von: Ankur Tagra, Haoxiang Zhang, Gopi Krishnan Rajbahadur, Ahmed E. Hassan

Erschienen in: Empirical Software Engineering | Ausgabe 4/2022

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Abstract

Reopened bugs can degrade the overall quality of a software system since they require unnecessary rework by developers. Moreover, reopened bugs also lead to a loss of trust in the end-users regarding the quality of the software. Thus, predicting bugs that might be reopened could be extremely helpful for software developers to avoid rework. Prior studies on reopened bug prediction focus only on three open source projects (i.e., Apache, Eclipse, and OpenOffice) to generate insights. We observe that one out of the three projects (i.e., Apache) has a data leak issue – the bug status of reopened was included as training data to predict reopened bugs. In addition, prior studies used an outdated prediction model pipeline (i.e., with old techniques for constructing a prediction model) to predict reopened bugs. Therefore, we revisit the reopened bugs study on a large scale dataset consisting of 47 projects tracked by JIRA using the modern techniques such as SMOTE, permutation importance together with 7 different machine learning models. We study the reopened bugs using a mixed methods approach (i.e., both quantitative and qualitative study). We find that: 1) After using an updated reopened bug prediction model pipeline, only 34% projects give an acceptable performance with AUC \(\geqslant \) 0.7. 2) There are four major reasons for a bug getting reopened, that is, technical (i.e., patch/integration issues), documentation, human (i.e., due to incorrect bug assessment), and reasons not shown in the bug reports. 3) In projects with an acceptable AUC, 94% of the reopened bugs are due to patch issues (i.e., the usage of an incorrect patch) identified before bug reopening. Our study revisits reopened bugs and provides new insights into developer’s bug reopening activities.
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Literatur
Agrawal A, Fu W, Chen D, Shen X, Menzies T (2019) How to” dodge” complex software analytics. IEEE Transactions on Software Engineering (TSE’19), pp 1–13
Agrawal A, Menzies T (2018) Is” better data” better than” better data miners”?. In: 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE’18). IEEE, pp 1050–1061
Al Dallal J, Morasca S (2014) Predicting object-oriented class reuse-proneness using internal quality attributes. Empir Softw Eng (EMSE’14) 19(4):775–821CrossRef
Altmann A, Toloşi L, Sander O, Lengauer T (2010) Permutation importance: a corrected feature importance measure. Bioinformatics 26(10):1340–1347CrossRef
Anvik J, Hiew L, Murphy GC (2006) Who should fix this bug?. In: Proceedings of the 28th international conference on Software engineering (ICSE’06), pp 361–370
Arellano AV (2019) Epidemiological disease surveillance using public media text mining. North Carolina State University
Artstein R, Poesio M (2008) Inter-coder agreement for computational linguistics. Comput Linguist 34(4):555–596CrossRef
Beckler DT, Thumser ZC, Schofield JS, Marasco PD (2018) Reliability in evaluator-based tests: using simulation-constructed models to determine contextually relevant agreement thresholds. BMC Med Res Methodol 18(1):1–12CrossRef
Biggers LR, Bocovich C, Capshaw R, Eddy BP, Etzkorn LH, Kraft NA (2014) Configuring latent dirichlet allocation based feature location. Empir Softw Eng (EMSE’14) 19(3):465–500CrossRef
Bortis G, Van Der Hoek A (2013) Porchlight: A tag-based approach to bug triaging. In: 2013 35th International Conference on Software Engineering (ICSE). IEEE, pp 342–351
Boslaugh S (2012) Statistics in a nutshell: A desktop quick reference. O’Reilly Media, Inc.
Caglayan B, Misirli AT, Miranskyy A, Turhan B, Bener A (2012) Factors characterizing reopened issues: a case study. In: Proceedings of the 8th International Conference on Predictive Models in Software Engineering, pp 1–10
Cerda P, Varoquaux G, Kégl B (2018) Similarity encoding for learning with dirty categorical variables. Mach Learn 107(8-10):1477–1494MathSciNetCrossRef
Chakraborty D, Elzarka H (2019) Advanced machine learning techniques for building performance simulation: a comparative analysis. J Build Perform Simul 12(2):193–207CrossRef
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357MATHCrossRef
Chen D, Fu W, Krishna R, Menzies T (2018) Applications of psychological science for actionable analytics. In: Proceedings of the 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp 456–467
Corazza A, Di Martino S, Maggio V, Scanniello G (2016) Weighing lexical information for software clustering in the context of architecture recovery. Empir Softw Eng (EMSE’16) 21(1):72–103CrossRef
Corbin JM, Strauss A (1990) Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociol 13(1):3–21CrossRef
da Costa DA, McIntosh S, Treude C, Kulesza U, Hassan AE (2018) The impact of rapid release cycles on the integration delay of fixed issues. Empir Softw Eng (EMSE’18) 23(2):835–904CrossRef
Denny M, Spirling A (2017) Text preprocessing for unsupervised learning: Why it matters, when it misleads, and what to do about it. When It Misleads, and What to Do about It (September 27, 2017)
Fu W, Menzies T, Shen X (2016) Tuning for software analytics: Is it really necessary?. Inf Softw Technol 76:135–146CrossRef
Ghotra B, McIntosh S, Hassan AE (2015) Revisiting the impact of classification techniques on the performance of defect prediction models. In: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE’15), vol 1. IEEE, pp 789–800
Ghotra B, McIntosh S, Hassan AE (2017) A large-scale study of the impact of feature selection techniques on defect classification models. In: 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR’17). IEEE, pp 146–157
Guo PJ, Zimmermann T, Nagappan N, Murphy B (2010) Characterizing and predicting which bugs get fixed: an empirical study of microsoft windows. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering (ICSE’10)-Volume 1, pp 495–504
Guo PJ, Zimmermann T, Nagappan N, Murphy B (2011) ”not my bug!” and other reasons for software bug report reassignments. In: Proceedings of the ACM 2011 conference on Computer supported cooperative work, pp 395–404
Hardeniya N, Perkins J, Chopra D, Joshi N, Mathur I (2016) Natural language processing: python and nltk. Packt Publishing Ltd
Hayes AF, Krippendorff K (2007) Answering the call for a standard reliability measure for coding data. Commun Methods Measur 1(1):77–89CrossRef
He H, Ma Y (2013) Imbalanced learning: foundations, algorithms, and applications. Wiley
Hébert A (2020) Estimation of road accident risk with machine learning. Ph.D. Thesis, Concordia University
Hemalatha I, Varma GP Saradhi, Govardhan A (2012) Preprocessing the informal text for efficient sentiment analysis. Int J Emerging Trends Technol Comput Sci (IJETTCS) 1(2):58–61
Herzig K, Nagappan N (2015) Empirically detecting false test alarms using association rules. In: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE’15), vol 2. IEEE, pp 39–48
Jalbert N, Weimer W (2008) Automated duplicate detection for bug tracking systems. In: 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN). IEEE, pp 52–61
Jiarpakdee J, Tantithamthavorn C, Hassan AE (2019) The impact of correlated metrics on the interpretation of defect models. IEEE Transactions on Software Engineering (TSE’19)
Jiarpakdee J, Tantithamthavorn C, Treude C (2018) Autospearman: Automatically mitigating correlated software metrics for interpreting defect models. In: 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME’18). IEEE Computer Society, pp 92–103
Jiarpakdee J, Tantithamthavorn C, Treude C (2020) The impact of automated feature selection techniques on the interpretation of defect models. Empir Softw Eng (EMSE’20) 25(5):3590–3638CrossRef
Kannan S, Gurusamy V (2014) Preprocessing techniques for text mining. Int J Comput Sci Commun Netw 5(1):7–16
Kaufman S, Rosset S, Perlich C, Stitelman O (2012) Leakage in data mining: Formulation, detection, and avoidance. ACM Trans Knowl Discov Data (TKDD’12) 6(4):1–21CrossRef
Lee D, Rajbahadur GK, Lin D, Sayagh M, Bezemer C-P, Hassan AE (2020) An empirical study of the characteristics of popular minecraft mods. Empir Softw Eng (EMSE’20) 25(5):3396–3429CrossRef
Lessmann S, Baesens B, Mues C, Pietsch S (2008) Benchmarking classification models for software defect prediction: A proposed framework and novel findings. IEEE Trans Softw Eng (TSE’08) 34(4):485–496CrossRef
Li H, Shang W, Adams B, Sayagh M, Hassan AE (2020) A qualitative study of the benefits and costs of logging from developers’ perspectives. IEEE Transactions on Software Engineering (TSE’20)
Lipton ZC (2018) The mythos of model interpretability. Queue 16(3):31–57CrossRef
Malhotra R, Khanna M (2017) An empirical study for software change prediction using imbalanced data. Empir Softw Eng (EMSE’17) 22(6):2806–2851CrossRef
McIntosh S, Kamei Y, Adams B, Hassan AE (2016) An empirical study of the impact of modern code review practices on software quality. Empir Softw Eng (EMSE’16) 21(5):2146–2189CrossRef
McMillan C, Grechanik M, Poshyvanyk D, Fu C, Xie Q (2011) Exemplar: A source code search engine for finding highly relevant applications. IEEE Trans Softw Eng (TSE’11) 38(5):1069–1087CrossRef
Méndez JR, Iglesias EL, Fdez-Riverola F, Díaz F, Corchado JM (2005) Tokenising, stemming and stopword removal on anti-spam filtering domain. In: Conference of the Spanish Association for Artificial Intelligence. Springer, pp 449–458
Meyer TA, Whateley B (2004) Spambayes: Effective open-source, bayesian based, email classification system.. In: CEAS. Citeseer
Mi Q, Keung J (2016) An empirical analysis of reopened bugs based on open source projects. In: Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, pp 1–10
Mi Q, Keung J, Huo Y, Mensah S (2018) Not all bug reopens are negative: A case study on eclipse bug reports. Inf Softw Technol 99:93–97CrossRef
Morasca S, Lavazza L (2020) On the assessment of software defect prediction models via ROC curves. Empir Softw Eng (EMSE’20) 25(5):3977–4019CrossRef
Murphy G, Cubranic D (2004) Automatic bug triage using text categorization. In: Proceedings of the Sixteenth International Conference on Software Engineering & Knowledge Engineering. Citeseer, pp 1–6
Nyamawe AS, Liu H, Niu N, Umer Q, Niu Z (2020) Feature requests-based recommendation of software refactorings. Empir Softw Eng (EMSE’20) 25(5):4315–4347CrossRef
Rajbahadur GK, Wang S, Ansaldi G, Kamei Y, Hassan AE (2021) The impact of feature importance methods on the interpretation of defect classifiers. IEEE Transactions on Software Engineering (TSE’21)
Rajbahadur GK, Wang S, Kamei Y, Hassan AE (2017) The impact of using regression models to build defect classifiers. In: 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR’17). IEEE, pp 135–145
Rajbahadur GK, Wang S, Kamei Y, Hassan AE (2019) Impact of discretization noise of the dependent variable on machine learning classifiers in software engineering. IEEE Trans Softw Eng (TSE’19):1–18
Rakha M S, Bezemer C-P, Hassan AE (2017) Revisiting the performance evaluation of automated approaches for the retrieval of duplicate issue reports. IEEE Trans Softw Eng (TSE’17) 44(12):1245–1268CrossRef
Rodríguez-Pérez G, Robles G, Serebrenik A, Zaidman A, Germán DM, Gonzalez-Barahona JM (2020) How bugs are born: a model to identify how bugs are introduced in software components. Empir Softw Eng (EMSE’20) 25(2):1294–1340CrossRef
Saha RK, Khurshid S, Perry DE (2015) Understanding the triaging and fixing processes of long lived bugs. Inf Softw Technol 65:114–128CrossRef
Scoccia GL, Autili M (2020) Web frameworks for desktop apps: an exploratory study. In: Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM’20), pp 1–6
Shihab E, Ihara A, Kamei Y, Ibrahim WM, Ohira M, Adams B, Hassan AE, Matsumoto K- (2010) Predicting re-opened bugs: A case study on the eclipse project. In: 2010 17th Working Conference on Reverse Engineering. IEEE, pp 249–258
Shihab E, Ihara A, Kamei Y, Ibrahim WM, Ohira M, Adams B, Hassan AE, Matsumoto Ki (2013) Studying re-opened bugs in open source software. Empir Softw Eng (EMSE’13) 18(5):1005–1042CrossRef
Somasundaram K, Murphy GC (2012) Automatic categorization of bug reports using latent dirichlet allocation. In: Proceedings of the 5th India software engineering conference, pp 125–130
Song F, Liu S, Yang J (2005) A comparative study on text representation schemes in text categorization. Pattern analysis and applications 8(1-2):199–209MathSciNetCrossRef
Srividhya V, Anitha R (2010) Evaluating preprocessing techniques in text categorization. Int J Comput Sci Appl 47(11):49–51
Stolberg S (2009) Enabling agile testing through continuous integration. In: 2009 agile conference. IEEE, pp 369–374
Tantithamthavorn C, Hassan AE, Matsumoto K (2018) The impact of class rebalancing techniques on the performance and interpretation of defect prediction models. IEEE Transactions on Software Engineering (TSE’18)
Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2016) Automated parameter optimization of classification techniques for defect prediction models. In: Proceedings of the 38th International Conference on Software Engineering (ICSE’16), pp 321–332
Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2016) An empirical comparison of model validation techniques for defect prediction models. IEEE Trans Softw Eng (TSE’16) 43(1):1–18
Tian Y, Sun C, Lo D (2012) Improved duplicate bug report identification. In: 2012 16th European Conference on Software Maintenance and Reengineering. IEEE, pp 385–390
Tian Y, Wijedasa D, Lo D, Le Goues C (2016) Learning to rank for bug report assignee recommendation. In: 2016 IEEE 24th International Conference on Program Comprehension (ICPC’16). IEEE, pp 1–10
Tu F, Zhu J, Zheng Q, Zhou M (2018) Be careful of when: an empirical study on time-related misuse of issue tracking data. In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp 307–318
Turabieh H, Mafarja M, Li X (2019) Iterated feature selection algorithms with layered recurrent neural network for software fault prediction. Expert Syst Appl 122:27–42CrossRef
Uysal AK, Gunal S (2014) The impact of preprocessing on text classification. Inf Process Manag 50(1):104–112CrossRef
Vassallo C, Panichella S, Palomba F, Proksch S, Gall HC, Zaidman A (2020) How developers engage with static analysis tools in different contexts. Empir Softw Eng (EMSE’20) 25(2):1419–1457CrossRef
Vieira R, da Silva A, Rocha L, Gomes JP (2019) From reports to bug-fix commits: A 10 years dataset of bug-fixing activity from 55 apache’s open source projects. In: Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering, pp 80–89
Webb JK, Keller KA, Welle K, Allender MC (2020) Evaluation of the inter-and intraindividual agreement of a pododermatitis scoring model in greater flamingos (phoenicopterus roseus). J Zoo Wildlife Med 51(2):379–384CrossRef
Xia X, Lo D, Ding Y, Al-Kofahi JM, Nguyen TN, Wang X (2016) Improving automated bug triaging with specialized topic model. IEEE Trans Softw Eng (TSE’16) 43(3):272–297CrossRef
Xia X, Lo D, Shihab E, Wang X, Zhou B (2015) Automatic, high accuracy prediction of reopened bugs. Autom Softw Eng (ASE’15) 22(1):75–109CrossRef
Xia X, Lo D, Wang X, Yang X, Li S, Sun J (2013) A comparative study of supervised learning algorithms for re-opened bug prediction. In: 2013 17th European Conference on Software Maintenance and Reengineering. IEEE, pp 331–334
Xia X, Lo D, Wang X, Zhou B (2013) Accurate developer recommendation for bug resolution. In: 2013 20th Working Conference on Reverse Engineering (WCRE). IEEE, pp 72–81
Xia X, Lo D, Wen M, Shihab E, Zhou B (2014) An empirical study of bug report field reassignment. In: 2014 Software Evolution Week-IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE’14). IEEE, pp 174–183
Xuan J, Jiang H, Hu Y, Ren Z, Zou W, Luo Z, Wu X (2014) Towards effective bug triage with software data reduction techniques. IEEE Trans Knowl Data Eng 27(1):264–280CrossRef
Xuan J, Jiang H, Ren Z, Zou W (2012) Developer prioritization in bug repositories. In: 2012 34th International Conference on Software Engineering (ICSE’12). IEEE, pp 25–35
Yadav A, Singh SK, Suri JS (2019) Ranking of software developers based on expertise score for bug triaging. Inf Softw Technol 112:1–17CrossRef
Yatish S, Jiarpakdee J, Thongtanunam P, Tantithamthavorn C (2019) Mining software defects: should we consider affected releases?. In: IEEE/ACM 41st International Conference on Software Engineering (ICSE’19). IEEE, pp 654–665
Zeng Y, Jiang K, Chen J (2019) Automatic seismic salt interpretation with deep convolutional neural networks. In: Proceedings of the 3rd International Conference on Information System and Data Mining, pp 16–20
Zhang H, Wang S, Chen T-H, Hassan AE (2020) Are comments on stack overflow well organized for easy retrieval by developers? ACM Trans Softw Eng Methodol (TOSEM’20) 29
Zhang H, Wang S, Chen T-H, Zou Y, Hassan AE (2019) An empirical study of obsolete answers on Stack Overflow. IEEE Transactions on Software Engineering (TSE’19)
Zheng A, Casari A (2018) Feature engineering for machine learning: principles and techniques for data scientists. O’Reilly Media, Inc.
Zimmermann T, Nagappan N, Guo PJ, Murphy B (2012) Characterizing and predicting which bugs get reopened. In: 2012 34th International Conference on Software Engineering (ICSE’12). IEEE, pp 1074–1083
Metadaten
Titel
Revisiting reopened bugs in open source software systems
verfasst von
Ankur Tagra
Haoxiang Zhang
Gopi Krishnan Rajbahadur
Ahmed E. Hassan
Publikationsdatum
01.07.2022
Verlag
Springer US
Erschienen in
Empirical Software Engineering / Ausgabe 4/2022
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI
https://doi.org/10.1007/s10664-022-10133-6

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