Stage-oriented Analysis on Factors Impacting Bug Fixing Time

Abstract

Abstract-The timely fixing of bugs is important to ensure software quality. In Open Source Software (OSS) development, behaviors of stakeholders impact the bug fixing process, especially the different stages respectively. However, most of the existing studies on impact factors of bug fixing time usually treat bug fixing process as a whole, while neglecting the particularity at its different stages. Ignoring the detail of different stages cannot let us understand why the fixing time is longer or shorter. In this paper, we aimed at investigating whether the factors have different impacts on the time of different stages and the whole process. Three stages of the whole fixing process were formalized, and twenty-four factors were defined and extracted from three aspects: bug reports, their associated source code and code changes. An empirical study based on two OSS projects, Eclipse JDT Core and Linux Kernel, was conducted for the investigation. The results of our study provide a very positive validation that the influence of factors on bug fixing time is stage related, rather than for the whole process. Our results can help developers better understand influences of factors on the bug fixing process, and thus provide opportunities to improve their process effectively.

Evolution of internal dimensions in object‐oriented software–A time series based approach

“…The software evolution literature has often used the time series approach to extract and propose prediction models. For instance, some studies in the literature have focused on proposing prediction models aiming to predict defects 38–43 . They mainly differ in the approach used to build the model.…”

“…al., 38 used Granger causality test. Raja et al, 39 Yazdi et al, 73 and Wu et al 43 applied ARIMA. Graves et al 40 and Arisholm and Briand 41 used linear regression techniques.…”

“…Besides, the time series approach has often been used in software engineering mainly for extracting and proposing prediction models. Some application scenarios as defect, 38–43 changes, 44 clones, 45 size, 46 complexity, 46 and quality of service (QoS) 47 predictions are some examples of substantial applications of time series analysis in software engineering.…”

Managing uncertain tasks in technology‐intensive project environments: A multi‐method study of task closure and capacity management decisions

“…In contrast, corrective maintenance predominantly focuses on fixing discovered software defects. In this context, the literature has focused on predicting incoming tasks (e.g., Wu, Zhang, Yang, & Wang, ), and task resolution times (Weiss et al, ; Zhang et al, ). In general, these studies do not focus—as our study does—on modeling the specific nature of task uncertainty, and the corresponding implications for decisions related to system capacity, task effort allocation, and task closure—with the aim of maximizing system productivity.…”

Automatic Bug Triaging via Deep Reinforcement Learning

“…Therefore, many researchers use machine learning to solve this problem. To improve the efficiency of bug triaging, automatic bug report assignment [8][9][10][11], as well as various automatic allocation methods, have been proposed [12][13][14][15][16]. John et al [17] reported that text classification methods can be employed in bug triaging; additionally, experimental studies proved the effectiveness of this method.…”