Empirically revisiting and enhancing automatic classification of bug and non-bug issues

doi:10.1007/s11704-023-2771-z

Front. Comput. Sci.

2024, Vol. 18

Issue (5) : 185207 https://doi.org/10.1007/s11704-023-2771-z

RESEARCH ARTICLE

Empirically revisiting and enhancing automatic classification of bug and non-bug issues

Zhong LI^1,², Minxue PAN^1,³(

), Yu PEI⁴, Tian ZHANG^1,², Linzhang WANG^1,², Xuandong LI^1,²

¹. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
². Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
³. Software Institute, Nanjing University, Nanjing 210093, China
⁴. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

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Abstract

A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems (ITSs). Although the existing approaches have shown promising performance, the different design choices, including the different textual fields, feature representation methods and machine learning algorithms adopted by existing approaches, have not been comprehensively compared and analyzed. To fill this gap, we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches. Our experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification, including: (1) Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification; (2) Word embedding with Long Short-Term Memory (LSTM) can better extract features from the textual fields in the issues, and hence, lead to better issue classification models; (3) There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues; (4) The performance of the issue classification model is not sensitive to the choices of ML algorithms. Based on our study outcomes, we further propose an advanced issue classification approach, DEEPLABEL, which can achieve better performance compared with the existing issue classification approaches.

Keywords issue tracking issue type prediction empirical study

Corresponding Author(s): Minxue PAN

Just Accepted Date: 05 June 2023 Issue Date: 11 August 2023

Cite this article:

Zhong LI,Minxue PAN,Yu PEI, et al. Empirically revisiting and enhancing automatic classification of bug and non-bug issues[J]. Front. Comput. Sci., 2024, 18(5): 185207.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-2771-z
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I5/185207

Fig.1 Example of a Jira issue from the HttpClient project with BugID “HttpClient-705”. Names are redacted due to data privacy concerns

Fig.2 The workflow of automatic issue classification

Tab.1 Detailed information on the dataset

H e r z i g D a t a s e t

Approach name	Textual field $x$	FRM for $f e x t$	ML algorithm for $f c l s$	Original dataset	Metrics
Antoniol	Description	TFM	NB,LR,DT	Own Dataset	$P r e c i s i o n$ and $r e c a l l$ for each issue type
Chawla	Title	TFM	Fuzzy Logic	$H e r z i g D a t a s e t$	$P r e c i s i o n$ , $r e c a l l$ and $F 1$ score in the granularity of micro-averaging
Pandey		TFM	NB, SVM, LR	$H e r z i g D a t a s e t$
Otoom		Reduced TFM	NB,SVM,RF	Own Dataset	Accuracy
Terdchanakul	The concatenation of title and description	n-gram IDF	LR,RF	$H e r z i g D a t a s e t$	$F 1$ score in the granularity of micro-averaging
Pingclasai		Topic Modeling	DT,NB,LR
Qin		Word Embedding with LSTM	Softmax Layer
Kallis		fastText	fastText	Own Dataset	$P r e c i s i o n$ , $r e c a l l$ and $F 1$ score in the granularity of micro-averaging
Herbold	Using separate models for title and description	fastText	fastText	$H e r z i g D a t a s e t$

Tab.2 Studied Approaches in this study. Column “Textual Field

x

” lists the textual field

x

used by each studied approach; Column “FRM for

f e x t

” lists the feature representation methods used to construct the feature extractor

f e x t

; Column “ML algorithm for

f c l s

” lists the ML algorithms used to build the classifier

f c l s

; Column “Dataset” lists the issue dataset in the paper of the approach, where “Own Dataset” denotes the dataset constructed by the approach itself; Column “Metrics” lists the evaluation metrics used in the paper of the approach

Fig.3

Approach	Bug			Non-Bug			Overall
Approach	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$
Chawla	0.73	0.49	0.59	0.77	0.90	0.83	0.76	0.76	0.75
Pandey	0.78	0.70	0.74	0.85	0.89	0.87	0.83	0.83	0.83
Otoom	0.24	0.03	0.05	0.64	0.94	0.76	0.51	0.63	0.52
Terdchanakul	0.58	0.33	0.42	0.71	0.87	0.78	0.67	0.68	0.66
Pingclasai	0.41	0.41	0.41	0.69	0.69	0.69	0.60	0.60	0.60
Qin	0.78	0.75	0.76	0.87	0.87	0.87	0.84	0.84	0.84
Kallis	0.78	0.71	0.74	0.85	0.88	0.87	0.83	0.83	0.83
Herbold	0.82	0.72	0.77	0.86	0.91	0.89	0.85	0.85	0.85

Tab.3 Performance of different Issue Classification Approaches. The Antoniol approach is not listed due to its feature selection can not terminate on

H e r z i g D a t a s e t

Approach	Strategy	Bug			Non-Bug			Overall
Approach	Strategy	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$
Chawla	$T i t l e$	0.73 (AB)	0.49 (A)	0.59 (A)	0.77 (A)	0.90 (B)	0.83 (A)	0.76 (A)	0.76 (A)	0.75 (A)
	$D e s c$	0.59 (C)	0.13 (B)	0.22 (B)	0.67 (B)	0.94 (A)	0.78 (B)	0.64 (B)	0.66 (B)	0.59 (B)
	$C o m b 1$	0.64 (B)	0.14 (B)	0.22 (B)	0.67 (B)	0.95 (A)	0.79 (AB)	0.66 (B)	0.67 (B)	0.59 (B)
	$C o m b 2$	0.75 (A)	0.50 (A)	0.60 (A)	0.77 (A)	0.91 (B)	0.83 (A)	0.77 (A)	0.77 (A)	0.76 (A)
Pandey	$T i t l e$	0.78 (A)	0.70 (A)	0.74 (A)	0.85 (A)	0.89 (A)	0.87 (A)	0.83 (A)	0.83 (A)	0.83 (A)
	$D e s c$	0.70 (C)	0.63 (B)	0.66 (C)	0.81 (B)	0.85 (B)	0.83 (C)	0.77 (B)	0.78 (B)	0.77 (B)
	$C o m b 1$	0.74 (B)	0.72 (A)	0.73 (B)	0.85 (A)	0.86 (B)	0.86 (B)	0.81 (AB)	0.81 (AB)	0.81 (AB)
	$C o m b 2$	0.79 (A)	0.71 (A)	0.75 (A)	0.85 (B)	0.90 (A)	0.87 (A)	0.84 (A)	0.84 (A)	0.84 (A)
Otoom	$T i t l e$	0.24 (B)	0.03 (?)	0.05 (B)	0.64 (?)	0.94 (?)	0.76 (?)	0.51 (?)	0.63 (?)	0.52 (?)
	$D e s c$	0.28 (A)	0.02 (?)	0.05 (B)	0.65 (?)	0.95 (?)	0.77 (?)	0.52 (?)	0.63 (?)	0.52 (?)
	$C o m b 1$	0.27 (A)	0.05 (?)	0.09 (A)	0.65 (?)	0.92 (?)	0.75 (?)	0.52 (?)	0.62 (?)	0.53 (?)
	$C o m b 2$	0.25 (B)	0.03 (?)	0.05 (B)	0.65 (?)	0.96 (?)	0.77 (?)	0.52 (?)	0.64 (?)	0.53 (?)
Terdchanakul	$T i t l e$	0.66 (B)	0.30 (B)	0.41 (?)	0.71 (?)	0.92 (A)	0.80 (A)	0.70 (A)	0.70(B)	0.67 (B)
	$D e s c$	0.58 (C)	0.35 (A)	0.43 (?)	0.71 (?)	0.86 (B)	0.78 (B)	0.67 (B)	0.69 (B)	0.66 (B)
	$C o m b 1$	0.58 (C)	0.33 (A)	0.42 (?)	0.71 (?)	0.87 (B)	0.78 (B)	0.67 (B)	0.68 (B)	0.66 (B)
	$C o m b 2$	0.69 (A)	0.29 (B)	0.40 (?)	0.71 (?)	0.93 (A)	0.81 (A)	0.71 (A)	0.71 (A)	0.70 (A)
Pingclasai	$T i t l e$	0.50 (B)	0.12 (B)	0.19 (C)	0.66 (?)	0.93 (A)	0.77 (B)	0.61 (B)	0.65 (B)	0.57 (C)
	$D e s c$	0.42 (C)	0.37 (A)	0.39 (A)	0.68 (?)	0.73 (B)	0.70 (C)	0.60 (B)	0.61 (C)	0.60 (B)
	$C o m b 1$	0.41 (C)	0.41 (A)	0.41 (A)	0.69 (?)	0.69 (B)	0.69 (C)	0.60 (B)	0.60 (C)	0.60 (B)
	$C o m b 2$	0.66 (A)	0.17 (B)	0.27 (B)	0.68 (?)	0.95 (A)	0.80 (A)	0.68 (A)	0.68 (A)	0.62 (A)
Qin	$T t i t l e$	0.77 (B)	0.72 (B)	0.74 (B)	0.86 (A)	0.88 (B)	0.87 (B)	0.83 (AB)	0.83 (AB)	0.83 (AB)
	$D e s c$	0.71 (C)	0.70 (C)	0.70 (C)	0.84 (B)	0.84 (C)	0.84 (C)	0.80 (B)	0.79 (B)	0.79 (B)
	$C o m b 1$	0.78 (B)	0.75 (A)	0.76 (A)	0.87 (A)	0.87 (B)	0.87 (B)	0.84 (A)	0.84 (A)	0.84 (A)
	$C o m b 2$	0.82 (A)	0.74 (A)	0.77 (A)	0.87 (A)	0.90 (A)	0.89 (A)	0.85 (A)	0.85 (A)	0.85 (A)
Kallis	$T i t l e$	0.77 (B)	0.72 (A)	0.74 (B)	0.86 (A)	0.88 (B)	0.87 (B)	0.83 (B)	0.83 (B)	0.83 (B)
	$D e s c$	0.74 (C)	0.62 (B)	0.67 (C)	0.81 (B)	0.88 (B)	0.84 (C)	0.79 (C)	0.79 (C)	0.79 (C)
	$C o m b 1$	0.78 (B)	0.71 (A)	0.74 (B)	0.85 (A)	0.88 (B)	0.87 (B)	0.83 (B)	0.83 (B)	0.83 (B)
	$C o m b 2$	0.82 (A)	0.72 (A)	0.77 (A)	0.86 (A)	0.91 (A)	0.89 (A)	0.85 (A)	0.85 (A)	0.85 (A)

Tab.4 Comparing different types of classifiers. The four types of classifiers for the Herbold approach are the same as the four types of classifiers for the Kallis approach and hence elided. The capital letters A-C in the parentheses are the results of the Tukey HSD test, where techniques are clustered into the letter groups, and A is the best while C is the worst (and having two letters means the technique is in a group that is between the two letter groups). Symbol “?” denotes the Tukey HSD test fails to observe a statistical difference between the four strategies

Fig.4 The detailed analysis of results on trategy Title and Strategy Desc. (a) The ratio of the number of issues that are classified differently by Strategy Title and Strategy Desc to the number of testing issues. (b) The ratio of the number of issues that are correctly predicted by Strategy Title (or Desc) to the number of issues that are classified differently by the two classifier types

Fig.5

Fig.6

Fig.7

Classifier	FRM	Bug			Non-Bug			Overall
Classifier	FRM	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$
LR	word embedding with LSTM	0.82 (A)	0.74 (A)	0.78 (A)	0.87 (A)	0.91 (B)	0.89 (A)	0.85 (A)	0.85 (A)	0.85 (A)
	fastText	0.73 (C)	0.38 (B)	0.50 (B)	0.74 (B)	0.93 (B)	0.82 (B)	0.74 (B)	0.74 (B)	0.71 (B)
	TFM	0.80 (B)	0.72 (A)	0.76 (A)	0.86 (A)	0.90 (C)	0.88 (A)	0.84 (A)	0.84 (A)	0.84 (A)
	reduced TFM	0.00 (E)	0.00 (E)	0.00 (E)	0.65 (C)	1.00 (A)	0.79 (C)	0.43 (D)	0.65 (C)	0.52 (D)
	topic modeling	0.66 (D)	0.17 (D)	0.27 (D)	0.68 (BC)	0.94 (B)	0.79 (C)	0.68 (C)	0.68 (C)	0.61 (C)
	n-gram IDF	0.72 (C)	0.25 (C)	0.37 (C)	0.70 (BC)	0.94 (B)	0.80 (B)	0.71 (BC)	0.70 (BC)	0.66 (BC)
RF	word embedding with LSTM	0.81 (A)	0.74 (A)	0.78 (A)	0.87 (A)	0.91 (C)	0.89 (A)	0.85 (A)	0.85 (A)	0.85 (A)
	fastText	0.80 (A)	0.18 (D)	0.30 (D)	0.69 (D)	0.97 (B)	0.80 (B)	0.73 (C)	0.70 (CD)	0.63 (D)
	TFM	0.81 (A)	0.65 (B)	0.72 (B)	0.83 (B)	0.92 (C)	0.87 (A)	0.83 (B)	0.83 (B)	0.82 (B)
	reduced TFM	0.28 (C)	0.00 (E)	0.00 (E)	0.65 (E)	0.99 (A)	0.78 (C)	0.54 (E)	0.65 (D)	0.51 (E)
	topic modeling	0.49 (C)	0.23 (C)	0.32 (D)	0.68 (D)	0.87 (D)	0.76 (D)	0.62 (D)	0.65 (D)	0.61 (D)
	n-gram IDF	0.69 (B)	0.28 (C)	0.40 (C)	0.71 (C)	0.93 (C)	0.80 (B)	0.70 (C)	0.71 (C)	0.67 (C)
DT	word embedding with LSTM	0.65 (A)	0.86 (A)	0.74 (A)	0.91 (A)	0.75 (C)	0.82 (A)	0.82 (A)	0.79 (A)	0.79 (A)
	fastText	0.41 (C)	0.66 (B)	0.50 (C)	0.73 (C)	0.49 (E)	0.59 (D)	0.62 (D)	0.55 (D)	0.56 (E)
	TFM	0.57 (B)	0.84 (A)	0.68 (B)	0.88 (B)	0.67 (D)	0.76 (B)	0.78 (B)	0.73 (B)	0.73 (B)
	reduced TFM	0.29 (D)	0.00 (E)	0.01 (E)	0.65 (D)	0.99 (A)	0.78 (AB)	0.53 (E)	0.64 (C)	0.51 (F)
	topic modeling	0.41 (C)	0.40 (C)	0.40 (D)	0.68 (CD)	0.69 (D)	0.68 (C)	0.59 (DE)	0.59 (D)	0.59 (D)
	n-gram IDF	0.68 (A)	0.28 (D)	0.39 (D)	0.71 (CD)	0.92 (B)	0.80 (AB)	0.70 (C)	0.70 (B)	0.66 (C)
Softmax	word embedding with LSTM	0.81 (A)	0.73 (B)	0.77 (A)	0.86 (A)	0.90 (B)	0.88 (A)	0.85 (A)	0.85 (A)	0.85 (A)
	fastText	0.33 (D)	0.94 (A)	0.49 (C)	0.32 (E)	0.01 (C)	0.02 (C)	0.33 (F)	0.33 (D)	0.18 (E)
	TFM	0.80 (A)	0.64 (C)	0.71 (B)	0.82 (B)	0.91 (B)	0.87 (A)	0.82 (B)	0.82 (B)	0.81 (B)
	reduced TFM	0.00 (E)	0.00 (E)	0.00 (E)	0.65 (D)	1.00 (A)	0.78 (B)	0.42 (E)	0.65 (C)	0.51 (D)
	topic modeling	0.52 (C)	0.03 (E)	0.07 (E)	0.65 (D)	0.98 (A)	0.78 (B)	0.62 (D)	0.66 (C)	0.54 (D)
	n-gram IDF	0.74 (B)	0.09 (D)	0.16 (D)	0.67 (C)	0.97 (AB)	0.79 (AB)	0.70 (C)	0.67 (C)	0.58 (C)

Tab.5 Comparing different feature representation methods. The capital letters A-F in the parentheses are the results of the Tukey HSD test, where techniques are clustered into the letter groups, and A is the best while F is the worst (and having two letters means the technique is in a group that is between the two letter groups). Symbol “?” denotes the Tukey HSD test fails to observe a statistical difference between the six feature representation methods

Fig.8

Fig.9

FRM	Classifier	Bug			Non-Bug			Overall
FRM	Classifier	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$
word embedding with LSTM	LR	0.82 (A)	0.74 (B)	0.78 (A)	0.87 (B)	0.91 (A)	0.89 (A)	0.85 (A)	0.85 (A)	0.85 (A)
	RF	0.81 (A)	0.74 (B)	0.78 (A)	0.87 (B)	0.91 (A)	0.89 (A)	0.85 (AB)	0.85 (A)	0.85 (A)
	DT	0.65 (B)	0.86 (A)	0.74 (B)	0.91 (A)	0.75 (B)	0.82 (B)	0.82 (B)	0.79 (B)	0.79 (B)
	Softmax	0.81 (A)	0.73 (B)	0.77 (AB)	0.86 (B)	0.90 (A)	0.88 (A)	0.85 (AB)	0.85 (A)	0.85 (A)
fastText	LR	0.73 (B)	0.38 (C)	0.50 (A)	0.74 (A)	0.93 (B)	0.82 (A)	0.74 (A)	0.74 (A)	0.71 (A)
	RF	0.80 (A)	0.18 (D)	0.30 (B)	0.69 (A)	0.97 (A)	0.80 (B)	0.73 (B)	0.70 (A)	0.63 (B)
	DT	0.41 (C)	0.66 (B)	0.50 (A)	0.73 (A)	0.49 (C)	0.59 (C)	0.62 (C)	0.55 (B)	0.56 (C)
	Softmax	0.33 (C)	0.94 (A)	0.49 (A)	0.32 (B)	0.01 (D)	0.02 (D)	0.33 (D)	0.33 (C)	0.18 (D)
TFM	LR	0.80 (A)	0.72 (B)	0.76 (A)	0.86 (B)	0.90 (A)	0.88 (A)	0.84 (A)	0.84 (A)	0.84 (A)
	RF	0.81 (A)	0.65 (C)	0.72 (AB)	0.83 (B)	0.92 (A)	0.87 (A)	0.83 (A)	0.83 (A)	0.82 (A)
	DT	0.57 (B)	0.84 (A)	0.68 (B)	0.88 (A)	0.67 (B)	0.76 (B)	0.78 (B)	0.73 (B)	0.73 (B)
	Softmax	0.80 (A)	0.64 (C)	0.71 (AB)	0.82 (B)	0.91 (A)	0.87 (A)	0.82 (AB)	0.82 (A)	0.81 (A)
reduced TFM	LR	0.00 (B)	0.00 (B)	0.00 (B)	0.65 (?)	1.00 (A)	0.79 (?)	0.43 (B)	0.65 (?)	0.52 (?)
	RF	0.28 (A)	0.00 (AB)	0.00 (AB)	0.65 (?)	0.99 (B)	0.78 (?)	0.54 (A)	0.65 (?)	0.51 (?)
	DT	0.29 (A)	0.00 (A)	0.01 (A)	0.65 (?)	0.99 (C)	0.78 (?)	0.53 (A)	0.64 (?)	0.51 (?)
	Softmax	0.00 (B)	0.00 (B)	0.00 (B)	0.65 (?)	1.00 (AB)	0.78 (?)	0.42 (B)	0.65 (?)	0.51 (?)
topic modeling	LR	0.66 (A)	0.17 (C)	0.27 (B)	0.68 (?)	0.94 (B)	0.79 (A)	0.68 (A)	0.68 (A)	0.61 (A)
	RF	0.49 (B)	0.23 (B)	0.32 (B)	0.68 (?)	0.87 (C)	0.76 (A)	0.62 (B)	0.65 (A)	0.61 (A)
	DT	0.41 (B)	0.40 (A)	0.40 (A)	0.68 (?)	0.69 (D)	0.68 (B)	0.59 (B)	0.59 (B)	0.59 (AB)
	Softmax	0.52 (AB)	0.03 (D)	0.07 (C)	0.65 (?)	0.98 (A)	0.78 (A)	0.62 (B)	0.66 (A)	0.54 (B)
n-gram IDF	LR	0.72 (?)	0.25 (A)	0.37 (A)	0.70 (?)	0.94 (B)	0.80 (?)	0.71 (?)	0.70 (?)	0.66 (A)
	RF	0.69 (?)	0.28 (A)	0.40 (A)	0.71 (?)	0.93 (B)	0.80 (?)	0.70 (?)	0.71 (?)	0.67 (A)
	DT	0.68 (?)	0.28 (A)	0.39 (A)	0.71 (?)	0.92 (B)	0.80 (?)	0.70 (?)	0.70 (?)	0.66 (A)
	Softmax	0.74 (?)	0.09 (B)	0.16 (B)	0.67 (?)	0.97 (A)	0.79 (?)	0.70 (?)	0.67 (?)	0.58 (B)

Tab.6 Comparing different machine learning algorithms. The capital letters A-D in the parentheses are the results of the Tukey HSD test, where techniques are clustered into the letter groups, and A is the best while D is the worst (and having two letters means the technique is in a group that is between the two letter groups). Symbol “?” denotes the Tukey HSD test fails to observe a statistical difference between the four machine learning algorithms

Fig.10

Fig.11 The overview of DEEPLABEL

Approach	Merformance metrics									Statistical testing
	Bug			Non-Bug			Overall			Bug			Non-Bug			Overall
	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$
Chawla	0.73	0.49	0.59	0.77	0.90	0.83	0.76	0.76	0.75	0.86	0.86	1.00	0.97	0.21	0.84	0.94	0.93	0.97
Pandey	0.78	0.70	0.74	0.85	0.89	0.87	0.83	0.83	0.83	0.49	0.54	0.67	0.58	0.30	0.40	0.69	0.65	0.67
Otoom	0.24	0.03	0.05	0.64	0.94	0.76	0.51	0.63	0.52	1.00	1.00	1.00	?0.64	1.00	0.94	1.00	1.00	1.00
Terdchanakul	0.58	0.33	0.42	0.71	0.87	0.78	0.67	0.68	0.66	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
Pingclasai	0.41	0.41	0.41	0.69	0.69	0.69	0.60	0.60	0.60	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
Qin	0.78	0.75	0.76	0.87	0.87	0.87	0.84	0.84	0.84	0.65	0.09	0.50	0.18	0.35	0.28	0.44	0.43	0.42
Kallis	0.78	0.71	0.74	0.85	0.88	0.87	0.83	0.83	0.83	0.48	0.44	0.68	0.50	0.26	0.40	0.63	0.58	0.60
Herbold	0.82	0.72	0.77	0.86	0.91	0.89	0.85	0.85	0.85	0.00	0.47	0.38	0.38	?0.05	0.16	0.30	0.29	0.30
BERT- $T i t l e$	0.77	0.78	0.77	0.88	0.87	0.87	0.84	0.84	0.84	0.62	?0.20	0.28	0.02	0.48	0.30	0.32	0.38	0.31
BERT- $D e s c$	0.72	0.70	0.71	0.84	0.85	0.84	0.80	0.80	0.80	0.88	0.56	0.90	0.66	0.66	0.70	0.82	0.82	0.82
BERT- $C o m b 1$	0.78	0.79	0.78	0.89	0.88	0.88	0.85	0.85	0.85	0.42	?0.34	0.16	?0.10	0.42	0.16	0.16	0.19	0.15
DEEPLABEL	0.82	0.77	0.79	0.88	0.91	0.89	0.86	0.86	0.86				?

Tab.7 Effectiveness comparison between DEEPLABEL and the previous techniques on

H e r z i g D a t a s e t

. Colum “Statistical Testing” presents the values of the Cliff’s delta effect size between

H e r z i g D a t a s e t

and each of the compared approaches. We highlight the statistical results using , and , when the Wilcoxon signed-rank test satisfy

p < 0.001

p < 0.01

p < 0.05

, respectively. Symbol “?” means not available in those cases

Fig.12

Tab.8 Detailed information on the dataset

L a r g e D a t a s e t

Approach	Performance metrics									Statistical testing
	Bug			Non-Bug			Overall			Bug			Non-Bug			Overall
	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$
Chawla	0.79	0.61	0.67	0.69	0.82	0.72	0.75	0.70	0.69	0.36	0.80	1.00	0.80	?0.70	0.80	1.00	1.00	1.00
Pandey	0.80	0.74	0.77	0.75	0.82	0.78	0.78	0.78	0.78	0.68	1.00	1.00	0.80	?0.24	0.62	1.00	1.00	1.00
Otoom	0.56	0.90	0.69	0.76	0.29	0.42	0.66	0.59	0.55	1.00	?0.88	1.00	0.8	1.00	1.00	1.00	1.00	1.00
Terdchanakul	0.67	0.61	0.63	0.64	0.70	0.66	0.67	0.65	0.65	1.00	1.00	1.00	1.00	0.84	1.00	1.00	1.00	1.00
Pingclasai	0.71	0.68	0.69	0.67	0.71	0.69	0.70	0.69	0.69	1.00	1.00	1.00	0.96	0.90	1.00	1.00	1.00	1.00
Qin	0.82	0.84	0.83	0.83	0.82	0.82	0.82	0.82	0.82	0.17	0.28	0.43	?0.12	?0.25	?0.17	0.26	0.23	0.24
Kallis	0.79	0.82	0.80	0.81	0.78	0.80	0.80	0.80	0.80	0.78	0.56	0.78	0.29	0.42	0.39	0.76	0.80	0.80
Herbold	0.81	0.84	0.82	0.82	0.79	0.80	0.81	0.81	0.81	0.48	0.34	0.52	0.27	0.44	0.43	0.58	0.62	0.60
BERT- $T i t l e$	0.81	0.83	0.82	0.83	0.82	0.82	0.82	0.82	0.82	0.43	0.46	0.62	0.07	0.13	0.05	0.49	0.48	0.48
BERT- $D e s c$	0.79	0.83	0.80	0.80	0.77	0.78	0.80	0.80	0.80	0.76	0.50	0.70	0.48	0.68	0.64	0.94	0.94	0.94
BERT- $C o m b 1$	0.82	0.85	0.84	0.84	0.83	0.84	0.83	0.83	0.83	0.2	0.18	0.34	?0.27	?0.44	?0.20	0.32	0.32	0.32
DEEPLABEL	0.85	0.86	0.86	0.83	0.82	0.83	0.85	0.85	0.85				?

Tab.9 Effectiveness comparison between DEEPLABEL and the previous techniques on

L a r g e D a t a s e t

. Colum “Statistical Testing” presents the values of the Cliff’s delta effect size between DEEPLABEL and each of the compared approaches. We highlight the statistical results using , and , when the Wilcoxon signed-rank test satisfy

p < 0.001

p < 0.01

p < 0.05

, respectively. Symbol “?” means not available in those cases

Fig.13

Approach	Overall performance			Statistical testing
Approach	$p r e c i s i o n$	$r e c a l l$	$F 1$	$p r e c i s i o n$	$r e c a l l$	$F 1$
Pandey	0.66	0.64	0.63	?0.16	0.18	0.19
Terdchanakul	0.31	0.38	0.27	1.00	1.00	1.00
Pingclasai	0.35	0.39	0.34	1.00	1.00	1.00
Qin	0.37	0.39	0.31	1.00	1.00	1.00
Kallis	0.48	0.56	0.48	1.00	0.98	1.00
Herbold	0.63	0.65	0.62	0.24	0.16	0.46
DEEPLABEL	0.65	0.66	0.65	?

Tab.10 Effectiveness comparison between DEEPLABEL and the previous techniques on multi-classification problem. The Chawla approach and the Otoom approach are not included in the table because they are not available for multi-classification problems. Colum “Statistical Testing” presents the values of the Cliff’s delta effect size between DEEPLABEL and each of the compared approaches. We highlight the statistical results using , and , when the Wilcoxon signed-rank test satisfy

p < 0.001

p < 0.01

p < 0.05

, respectively. Symbol “?” means not available in those cases

Tab.11 The words on which the attention mechanism applies the largest attention weights when detecting bug issues. The first row presents the words from the issue titles, and the second row presents the words from the issue descriptions

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