Exploring topic models in software engineering data analysis: A survey

2016 ◽  
Author(s):  
Xiaobing Sun ◽  
Xiangyue Liu ◽  
Bin Li ◽  
Yucong Duan ◽  
Hui Yang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Software Engineering ◽  
Topic Models ◽  
Engineering Data

Towards improving statistical modeling of software engineering data: think locally, act globally!

2014 ◽  
Vol 20 (2) ◽  
pp. 294-335 ◽  
Author(s):  
Nicolas Bettenburg ◽  
Meiyappan Nagappan ◽  
Ahmed E. Hassan
Keyword(s):  
Software Engineering ◽  
Statistical Modeling ◽  
Engineering Data

Intelligent Analysis of Software Maintenance Data

2009 ◽  
pp. 189-221
Author(s):  
Marek Reformat ◽  
Petr Musilek ◽  
Efe Igbide
Keyword(s):  
Software Engineering ◽  
Software Maintenance ◽  
Evidence Theory ◽  
Data Models ◽  
Ensemble Prediction ◽  
Software Companies ◽  
Single Defect ◽  
Engineering Data ◽  
Single Data ◽  
Prediction Systems

Amount of software engineering data gathered by software companies amplifies importance of tools and techniques dedicated to processing and analysis of data. More and more methods are being developed to extract knowledge from data and build data models. In such cases, selection of the most suitable data processing methods and quality of extracted knowledge is of great importance. Software maintenance is one of the most time and effort-consuming tasks among all phases of a software life cycle. Maintenance managers and personnel look for methods and tools supporting analysis of software maintenance data in order to gain knowledge needed to prepare better plans and schedules of software maintenance activities. Software engineering data models should provide quantitative as well as qualitative outputs. It is desirable to build these models based on a welldelineated logic structure. Such models would enhance maintainers’ understanding of factors which influence maintenance efforts. This chapter focuses on defect-related activities that are the core of corrective maintenance. Two aspects of these activities are considered: a number of software components that have to be examined during a defect removing process, and time needed to remove a single defect. Analysis of the available datasets leads to development of data models, extraction of IF-THEN rules from these models, and construction of ensemble-based prediction systems that are built based on these data models. The data models are developed using well-known tools such as See5/C5.0 and 4cRuleBuilder, and a new multi-level evolutionary-based algorithm. Single data models are put together into ensemble prediction systems that use elements of evidence theory for the purpose of inference about a degree of belief in the final prediction.

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