Authors : Tao Xie , Suresh Thummalapenta , David Lo , Chao Liu Authors Info & Claims
Pages 55 - 62 Published : 01 August 2009 Publication History 31 citation 0 Downloads Total Citations 31 Total Downloads 0 Last 12 Months 0 Last 6 weeks 0 Get Citation AlertsThis alert has been successfully added and will be sent to:
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To improve software productivity and quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks. However, mining SE data poses several challenges. The authors present various algorithms to effectively mine sequences, graphs, and text from such data.
The number, variety and complexity of projects involving data mining or knowledge discovery in databases activities have increased just lately at such a pace that aspects related to their development process need to be standardized for results to be .
Abstract The role of data mining in software engineering is obvious, but there is a lack of mining depth. In the past, engineering layout and function distribution problems in software development, and the framework construction was unreasonable. Therefore.
Software engineering processes are complex, and the related activities often produce a large number and variety of artefacts, making them well-suited to data mining. Recent years have seen an increase in the use of data mining techniques on such .
Reviewer: Alexis Leon
Nowadays, almost every aspect of life is touched and controlled by software. As software becomes a more prominent presence, the task of developing it becomes more difficult. Because software is used for critical applications and for controlling sophisticated equipment and systems, even a small bug can potentially have catastrophic consequences. Today's software projects are becoming more complex in size, sophistication, and the technologies used. As demand for software increases, software developers must find ways to improve their productivity, efficiency, and the quality of their product. This paper explores the use of data mining algorithms to perform software engineering (SE) tasks, and improve the quality and productivity of software developers. Organizations have large amounts of SE data, such as documents, reports, source code, and bug reports; mining this data can help organizations solve many SE problems. Data mining algorithms can help software engineers find the correct usage of an application programming interface (API), the impact of a change in source code, and potential bugs in the software. This paper explains the mining technology, the various challenges of mining SE data, and common data mining algorithms. Using examples, Xie et al. explain the various data, graph, and text mining algorithms for mining SE data: iterative patterns, temporal rules, sequence diagrams, finite state machines, sequence association rules, discriminative graph mining, graph classification to assist in debugging, and an algorithm to detect duplicate bug reporting. The results from their experiments illustrate the superiority of these techniques. This excellent paper will be useful to software engineers, productivity improvement professionals, and people and organizations that develop tools for improving the quality and efficiency of software development. Online Computing Reviews Service
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