Cost-Aware Triage Ranking Algorithms for Bug Reporting Systems
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1 Cost-Aware Triage Ranking Algorithms for Bug Reporting Systems Jin-woo Park 1, Mu-Woong Lee 1, Jinhan Kim 1, Seung-won Hwang 1, Sunghun Kim 2 POSTECH, 대한민국 1 HKUST, Hong Kong 2
2 Outline 1. CosTriage: A Cost-Aware Triage Algorithm for Bug Reporting Systems, Association for the Advancement of Artificial Intelligence (AAAI), 2011 Information & Database Systems Lab 2. CosTriage+: Cost-Aware Triage Ranking Algorithms for Bug Reporting Systems (Submitted), Distributed and Parallel Databases (DPD), 2012
3 Bug reporting systems Bugs!! More than 300 bug reports per day in Mozilla (a big software project) Information & Database Systems Lab Bug Solving One of the important issues in a software development process Bug reports are posted, discussed, and assigned to developers Open sources projects Apache Eclipse Linux kernel Mozilla
4 Bug reporting systems Bug reports Has Bug ID, title, description, status, and other meta data Assigned to developers and fixed by developers Information & Database Systems Lab Challenges Bug triage Duplicate bug detection bug ID status title (summary) bug fix history (time) other data description
5 Bug reporting systems Bug Triage Assigning a new bug report to a suitable developer Bottleneck of bug fixing process Labor intensive Miss-assignment can lead to slow bug fix Open Source Project Bug Reports Triager Bottleneck of the bug fixing process assign Can be automated!!! Developers
![Preliminary (Bug triage) PureCBR [Anvik06] Construct multi-class classifier](/docs-images/93/114062968/images/6-0.jpg "using a SVM classifier Bug reports B are converted into pair <Word vector,")
6 Preliminary (Bug triage) PureCBR [Anvik06] Construct multi-class classifier using a SVM classifier Bug reports B are converted into pair <Word vector, Dev> for training Bug Report History Input Data Developers Classes Bug Fix History training Assign a new bug to Dev 1 New Bug Report Classifier s scores
7 Preliminary (Bug triage) PureCBR [Anvik06] Good performances Problem This approach only considers accuracy Are developer happy? We consider developer s cost (e.g., interests, bug fix time, and expertise) We assume that faster bug fixing time has higher developer cost Each developer has different preference for a bug Bug Report 1 Bug Report 2 50 days 2 days
8 Preliminary (recommendation) Recommender algorithms Content-based recommendation (CBR) Predicting user s interests based on item features Machine learning methods Over-specialization problem Collaborative filtering recommendation (CF) Predicting user s interests based on affinity s interests for items User neighborhood Sparsity problem Hybrid recommendation Content-boosted collaborative filtering (CBCF) Combining an existing CBR with a CF Better performance than either approach alone
9 Preliminary (recommendation) Recommender algorithms Hybrid recommendation Content-boosted collaborative filtering (CBCF) Combining an existing CBR with a CF Better performance than either approach alone Two Phases - CBR phase - CF Phase Bug Report 5 Feature word 1 word 2 word 3 Count Bug 1 Bug 2 Bug 3 Bug 4 Bug 5 Dev 1 10???? Dev 2? 8 3?? Dev 3??? 7?
10 Preliminary (recommendation) Recommender algorithms Hybrid recommendation Content-boosted collaborative filtering (CBCF) Combining an existing CBR with a CF Better performance than either approach alone CBR phase Bug Report 5 Bug 1 Bug 2 Bug 3 Bug 4 Bug 5 Dev Dev Dev
11 Preliminary (recommendation) Recommender algorithms Hybrid recommendation Content-boosted collaborative filtering (CBCF) Combining an existing CBR with a CF Better performance than either approach alone CF phase Bug Report 5 Bug 1 Bug 2 Bug 3 Bug 4 Bug 5 Dev Dev Dev Existing recommendation approaches are not suitable!
12 Goal Goal Find efficient bug-developer matching Optimizing not only accuracy but also cost Use modified CBCF approach Constructing developer profiles for cost Challenge Enhancing CBCF approach for sparse data Extreme sparseness of the past bug fix history data A bug fixed by a developer Need to reduce sparseness for enhancing quality of CBCF Bug fix time from bug fix history
13 Overview Merging classifier s scores and developer s cost scores. The accuracy scores are obtained using PureCBR [Anvik06] The developer cost scores are obtained from de-sparsified bug fix history. Two scores are then merged for prediction New Bug Report Cost Developer profiles Accuracy Cost score Aggregation Recommended Developer Bug classifier <SVM> Accuracy score
14 CosTriage (Cost Estimation) CosTriage: A Cost-aware Triage Algorithm for bug reporting system Challenge to estimate the developer cost? How to reduce the sparseness problem? Using a Topic Modeling Bug fix time from bug fix history Categorization bugs to reduce the sparseness
![CosTriage Categorizing bugs Topic Modeling Latent Dirichlet Allocation (LDA) [BleiNg03] Each topic is represented as a bug type The topic distribution of reports](/docs-images/93/114062968/images/15-0.jpg "determine bug types We adopt the divergence measure proposed in [Arun, R.")
15 CosTriage Categorizing bugs Topic Modeling Latent Dirichlet Allocation (LDA) [BleiNg03] Each topic is represented as a bug type The topic distribution of reports determine bug types We adopt the divergence measure proposed in [Arun, R. PAKDD 10] Finding the natural number of topics (# bug types) t is the natural number of bug types
16 CosTriage Developer profiles modeling Developer Profiles N-dimensional feature vector The element of developer profiles, Pu[i], denotes the developer cost for i thtype bugs Developer Cost The average time to fix i th type bugs
17 CosTriage Predicting missing values in profiles Using CF for developer profiles Similarity measure: Information & Database Systems Lab k=1
18 CosTriage Obtaining developer s cost for a new bug report Information & Database Systems Lab Bug type = 1 New Bug Report Developer cost for a new bug
19 CosTriage Merging classifier s scores and developer s cost scores. Cost Information & Database Systems Lab Bug Reports Developer profiles <Bug types> Accuracy Bug classifier <SVM> Cost score Aggregation Accuracy score Recommended Developer Accuracy scores [Anvik06] + = Cost scores (CosTriage) Hybrid scores
20 CosTriage Limitations of CosTriage CosTriage cannot determine the bug types of some bug reports The report does not include any topic word identified by LDA model 4.04% bug reports in Mozilla projects CosTriage determines the bug types for the reports randomly. CosTriage does not consider temporal characteristic on modeling developer profiles Several studies show that a user s interests do change over time The recent bug fix history is more important than the older history
21 CosTriage+ Overcoming the limitations of CosTriage Code-information-based type prediction approach To determine the types of undetermined bug the reports Using a set of classes of libraries imported in the code Modeling developer profile changes over time To reduce the weight of bug fix history with a rate proportional to a period time Using exponential decay model
22 CosTriage+ Code-information-based type prediction approach Information & Database Systems Lab Using a set of classes or libraries imported in the code Set similarity Tree similarity (tree edit distance)
23 CosTriage+ Code-information-based type prediction approach Information & Database Systems Lab Using a set of classes or libraries imported in the code Set similarity Tree similarity (tree edit distance)
24 CosTriage+ Code-information-based type prediction approach Information & Database Systems Lab Using a set of classes or libraries imported in the code Set similarity Tree similarity (tree edit distance)
25 CosTriage+ Modeling developer profile changes over time Exponential decay Reducing weight of bug fix history with a rate proportional to a period time The quantity N decreases according to the following low: Quantifying developer s cost for i th-type bugs as the weight of bug history using exponential decay bug fix time weight
26 Experiments Subject Systems 97,910 valid bug reports 255 active developers From four open source projects Approaches PureCBR: State of the art CBR-based approach CBCF: Original CBCF CosTraige CosTraige+
27 Experiments Two research questions Q1. How much can our approach improve cost (bug fix time) without sacrificing bug assignment accuracy? Information & Database Systems Lab Q2. What are the trade-offs between accuracy and cost (bug fix time)? Evaluation measures W is the set of bug reports predicted correctly. N is the number of bug reports in the test set. The real fix time is unknown, we only use the fix time for correctly matched bugs.
28 Experiments Relative errors of expected bug fix time Information & Database Systems Lab Improvement of bug fix time (Q1) CosTriage+ improves the costs efficiently up to 31.49% without seriously compromising accuracy
29 Experiments Trade-off between accuracy and bug fix time (Q2) Information & Database Systems Lab
30 Experiments Using code information for type prediction 558 Mozilla reports (which have code) Models Random n-gram E.g., documents ( document in LDA model) Document d, do, doc, docu, ocum, cume, umen, ment, ent, nt, t Documents d, do, doc, docu, ocum, cume, umen, ment, ents, nts, ts, s Code-information-based approach Set similarity Tree similarity
31 Conclusion We proposed a new bug triaging technique Optimize not only accuracy but also cost Solve data sparseness problem by using topic modeling Information & Database Systems Lab We enhanced the approach Enlarging coverage of bug types Modeling developer profiles changes over time Experiments using four real bug report corpora Improve the cost without heavy losses of accuracy
32 Q & A Information & Database Systems Lab Thank you! Do you have any questions?
33 Back up We adopt the divergence measure proposed in [Arun10] Finding the natural number of topics (# bug types) Information & Database Systems Lab t is the natural number of bug types Mozilla Apache
34 Back up - Bug features Bug features Keywords of title and description Other meta data Information & Database Systems Lab New Bug Report Title : Traditional Memory Rendering refactoring request Description : Request additional refactoring so we can Traditional Rendering. Remove stopwords Traditional Memory Rendering refactoring request Request refactoring Traditional Rendering
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