Background: Bug reports are considered as a reference document, during the maintenance
phase of the software development process. The developer's counsel them at whatever point
they have to know about the reported bug and need to explore past bug solution. This process requires
a sustainable amount of time due to a large number of comments. Therefore, the best solution
to prevent the developers from reading the whole bug report is to summarize the entire discussion in
a couple of sentences. Bug report summarization is the extraction of some important part of bug reports
that are useful for investigation, resolving the bug with similar problems, reproducing the bug
and checking the status of the bug. If the bug reports have huge volume, variety and velocity information
as big data, extractive bug report summarization would be emerging as an issue. However,
the examiners, find out the bug report summaries do not meet the expectation of the developer; there
is a still need for reading the entire discussion.
Objective: (1) To generate generalized unsupervised extractive bug report summarization system,
which is easily applicable on any dataset without the need of effort and cost of manually creating
summaries for training dataset (2) To handle the extensive number of comments and generate short
summaries. (3) To reduce the data sparsity, reduction of information, redundancy and convergence
issue for short and lengthy data set. (4) To achieve the semantic summarization solution and explore
the large search space. (5) To provide the facility of adjusting the summary percentage.
Method: Particle swarm optimization and Hybridization of Ant Colony Optimization and Particle
Swarm Optimization approaches are used with the advantage of feature weighting technique.
Conclusion: The efficiency of the proposed approaches are compared with the best existing supervised
and unsupervised approaches. The result shows that the Hybrid swarm intelligence approach