# Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study
## 4 Systematic Mapping Process
### Q2 What Are the Quality Attributes for Threshold Calculation Methods? There are different quality attributes of threshold calculation methods that have been addressed in the research studies. As shown in Table 6, we divided them into five categories: fault detection, bad smells detection, reuse-proneness, other design problems, and none when no quality attribute was targeted. The method suggested by [PERSON] et al. [38] has various applications, including fault prediction and bad smell detection. Software system design, maintenance, and testing are the quality attributes for the object-oriented metrics that appeared in the paper of [PERSON] [62], which proposes a method based on the regression models. [PERSON] and [PERSON] [17] studied the evolutionary design process. Furthermore, they investigated the relationships between bad smells and class-error probability. [PERSON] and [PERSON] [35] provided an outlier detection algorithm used to improve the performance of fault predictors in the software engineering domain. The application domains for the threshold calculation method in the paper by [PERSON] et al. [14] are software quality and maintainability assessments, and they are especially useful for code smell detection rules. The scope of the paper by [PERSON] et al. [11] can determine the source of code discrepancies. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Quality Attributes** & **Papers** & **\#** \\ \hline Fault detection & [1, 16, 17, 18, 25, 26, 35, 38, 39, 43, 55, 60, 62, 64, 65, 66] & 16 \\ Bad Smells detection & [14, 34, 52, 58, 59, 63] & 6 \\ Design problems & [2, 11, 15, 24, 33, 40, 42, 53, 56] & 10 \\ Reuse-proneness & [61] & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of quality attributes under investigation. \begin{table}
\begin{tabular}{l c} \hline \hline
**Technique** & **Papers** & **\#** \\ \hline Programmer experience & [3, 33, 34, 35, 36] & 5 \\ Statistical properties from a corpus & [2, 7, 11, 13, 15, 22, 23, 25, 38, 41, 42, 44, 49, 50, 56, 57, 58, 59, 60, 61, 62, 63] & 23 \\ Quality related & [1, 14, 16, 17, 18, 24, 26, 38, 40, 52, 53, 55, 64, 65, 66] & 16 \\ Review study & [27] & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of thresholds kinds. [PERSON] et al. (2010) introduced a new cohesion metric (SCC) for software. These metrics let early design artifacts be evaluated at early phases. Moreover, in another research article [52], [PERSON] proposed a model to predict classes that require the Extract Subclass Refactoring (ESR) and presents these classes for improvement. In 2011, this author published two papers [24; 53]. In the first one, he defined the values of cohesion metrics, which considerably improved the detection of fault-prone classes. Furthermore, in the second paper, he extended the definition of the lack of cohesion (LCOM) metric as transitive LCOM (TLCOM), which improves its quality in predicting faulty class and in indicating class cohesion. As industrial applications, the thresholds derived with the method in the paper by [PERSON] et al. [7], have been successfully utilized for software analysis, benchmarking, and certification. [PERSON] [25] proposed an approach using log transformation to reduce data skewness. Log transformation has affected how metrics are used in reviewing software quality, such as threshold derivation and fault prediction. In another research, [PERSON] et al. [17] identified threshold values of CK metrics, which classify classes into several risk levels. [PERSON] et al. [36] analyzed a subset of object-oriented metrics as well as traditional metrics to compute system reliability and complexity for real-life applications. Prediction of fault-prone classes based on object-oriented metrics threshold values in earlier phases of the software development life cycle was the subject of study by [PERSON] and [PERSON] [26]. This early prediction helps developers to allocate necessary resources accordingly. [PERSON] et al.