# Graph-Driven Exploration of Issue Handling Schemes in Software Projects
## 5 Experimental Results and Analysis
### Comment Sequences  
The issue handling process is supported by comment exchanges between project stakeholders. They facilitate explaining sources of uncertainty, e.g., the imprecise specification of reported problems, their localization, the context of appearance, etc. In [22], we discussed issue comment classification based on machine learning schemes. Combining this classification algorithm with the IHM allowed us to derive comment sequence profiles as defined in Section 4.3. As an illustration, we present some results referring to the MongoDB project. We identified 722 unique comment sequences which appeared in 7156 issues within the repository, comprising 8405 bug issues, and hence 84.7% of issues comprised comments. In the APC global profile (Equation (7a)), short sequences dominated, with one to five comments. The distribution of comment sequence length was as follows: 1--4555; 2--1103; 3--569; 4--295; 5--176. These instances covered 6698 issues (80%). The longest comment sequence involved 57 comments (26 questions, 17 information and 14 positive) for a single issue; the first comment appeared about 30 min just after its registration (parameter SD), and the last comment appeared after 538 days. The third quartile of comment sequence duration (parameter CD) relevant to the 10 sequences with the highest issue coverage (5551) was in the range of 4-21 days. The APC profile of the 20 comment sequences related to the highest number of issues (restriction R1) was as follows:  
_APC1 R1 = {1, <fix>, 4016; 1, <information>, 366; 2, <information, fix>, 355; 2, <fix, fix>, 348; 3, <fix, fix, fix>, 120; 2, <positive, fix>, 95; 1, <positive>, 95; 2, <information, information>, 78; 1, <question>, 78; 3, <information, information, fix>, 71; 2, <question, fix>, 53; 4, <fix, fix, fix>, 48; 3, <question, information, fix>, 42; 3, <information, fix>, fix>, 30; 3, <information, positive, fix> 29; 2, <information, positive>, 27; 2, <question, question>, 22; 4, <information, information, information, fix>, 21; 3, question, information, information>, 21_  
The time (Q3 quartile) of the first comment appearance (CS) was in the range of several to 20 days, while sequence duration (CD) was typically in the range of several to a few dozen days. The maximal values of CS reached several years (postponed or neglected issues), the maximal values of CD reached several hundred days (appearing sporadically), while the Q3 quartile of CD correlated with the issue handling times in paths. A reduction in the number of comments requires accurate issue descriptions, better issue triaging, and careful selection of the project team, including systematic training and adapting to project specificities. This problem can also be studied in correlation with the APC(S\({}_{\text{r}}\)) and APC(P\({}_{\text{r}}\)) profiles defined in Section 4.3. Analyzing the comment sequences reported during handling states (APC(S\({}_{\text{r}}\))), we found 14 states (5 with low issue coverage among all 19) with comment sequences. As an illustration, we present the APC(S\({}_{\text{r}}\)) profile for the state _In code review_:  
_APC(In Code Review) | Ra = {1, <fixx>, 4046; 2, <fixx>, fix>, 261; 3, <fix, fixx>, 55; 2, <information, fixx>, 54; 1, <information>, 40; 4, <fix, fix, fixx> 16; 1, <positive> 14, 2, <fix, information>, 11);_  
The restriction condition Ra selects comment sequences covering more than 10 issues. We can also use aggregated statistics for states with many sequences:  
_Needs verification_: 173 unique sequences covering 1000 issues, constituting 12% of all (7765) issues handled in this state; Q3(CD)--4 days;  
_Open_: 107 unique sequences covering 952 issues, constituting 12.5% of all 7615 issues; Q3(CD)--14 days;  
_Investigating_: 96 unique sequences covering 658 issues, constituting 49% of all 1334 issues; Q3(CD)--10.6 days. Most issues were commented upon in the states _Needs merge_ (91%), _In code review_ (83%), and _Investigating_ (49%) due to the high effort related to these activities.