## 8 Related Work  
The prediction of just-in-time software defects helps to determine the buggy modules at such an earlier period and continuously once a change in software code is made [7,8,14]. The key benefits of JIT-SDP are that it is easy to anticipate buggy changes that are mapped to small parts of the code in order to save a lot of effort. It also makes it easier to identify who made the change, saving time identifying the developer who initiated the defect. Different granularity levels are categorized, such as software system, subsystem, component, package, file, class, method, or change in code. Since the smaller granularity will help practitioners lower their efforts, this contributes to cost-effectiveness. The prediction of defects at finer-grained levels has been researched to improve cost-effectiveness [7,14,49]. In many software defect datasets, class ratio of defect to non-defect is typically unbalanced. The unbalanced datasets result in poor prediction model [50]. The problem is called class imbalance. Class imbalance learning can improve performance by dealing with class imbalance at the instance-level or algorithm-level [29,51,52,53]. On the other hand, because researchers faced difficulties accessing industrial defect datasets because of data protection and privacy, only a few industrial applications of SDP were reported. Moreover, application of HS-based parameter optimization to both industry and JIT-SDP are the first case to the best of our knowledge. [PERSON] et al. [7] proposed a just-in-time defect prediction technique with a large-scale empirical analysis that offers fine granularity. For the prediction model, a regression model was used, based on data derived from a total of 11 projects (i.e., six open source software and five commercial software). Fourteen change-level metrics grouped into five kinds were used: three kinds of code size, four kinds of diffusion, three kinds of history, three kinds of experience, and one kind of purpose. However, any parameter optimization were not considered. Balance was not considered as well. In this article, we also use the same 14 change level metrics, but the maritime industrial dataset was selected, experimented with HS-based parameter optimization with balanced fitness value. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**DT**} & \multicolumn{3}{c}{**DT+HS**} & \multicolumn{3}{c}{**DT+BG+HS**} \\ \cline{2-10}  & **PD** & **PF** & **B** & **PD** & **PF** & **B** & **PD** & **PF** & **B** \\ \hline
**A1** & 0.33 & 0.08 & 0.51 & 0.75 & 0.10 & 0.77 & **0.83** & 0.15 & **0.82** \\
**A2** & 0.72 & 0.09 & 0.77 & **0.92** & 0.20 & 0.84 & 0.88 & 0.06 & **0.90** \\
**A3** & 0.88 & 0.13 & 0.85 & 0.87 & 0.10 & 0.87 & **0.92** & 0.07 & **0.91** \\
**A4** & 0.87 & 0.09 & 0.86 & 0.88 & 0.08 & 0.88 & **0.95** & 0.08 & **0.93** \\
**A5** & 0.90 & 0.12 & 0.87 & 0.94 & 0.11 & 0.89 & **0.95** & 0.06 & **0.94** \\
**B1** & 0.72 & 0.03 & 0.79 & 0.85 & 0.06 & 0.88 & **0.93** & 0.08 & **0.92** \\
**B2** & 0.83 & 0.08 & 0.86 & 0.93 & 0.08 & 0.92 & **0.94** & 0.05 & **0.95** \\
**B3** & 0.86 & 0.08 & 0.88 & 0.92 & 0.06 & 0.92 & **0.96** & 0.03 & **0.96** \\
**B4** & 0.93 & 0.06 & 0.93 & 0.90 & 0.03 & 0.92 & **0.99** & 0.04 & **0.97** \\
**B5** & 0.91 & 0.10 & 0.90 & 0.92 & 0.04 & 0.93 & **0.98** & 0.04 & **0.97** \\
**BU** & 0.57 & 0.15 & 0.68 & 0.64 & 0.26 & 0.69 & **0.77** & 0.29 & **0.73** \\
**CO** & 0.50 & 0.24 & 0.61 & 0.42 & 0.12 & 0.58 & **0.75** & 0.32 & **0.71** \\
**JD** & 0.31 & 0.13 & 0.50 & 0.30 & 0.12 & 0.50 & **0.66** & 0.28 & **0.69** \\
**PL** & 0.22 & 0.05 & 0.45 & 0.32 & 0.10 & 0.52 & **0.75** & 0.30 & **0.72** \\
**MO** & 0.34 & 0.13 & 0.52 & 0.21 & 0.04 & 0.44 & **0.72** & 0.20 & **0.75** \\
**PO** & 0.48 & 0.18 & 0.61 & 0.47 & 0.15 & 0.61 & **0.73** & 0.24 & **0.74** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Effect of bagging classifier. [PERSON] et al. [54] addressed the implementation of SDP to a Turkish telecommunication firm as part of a software measurement initiative.