How can we find data for quality prediction? Early in the life cycle, projects may lack the data needed to build such predictors. Prior work assumed that relevant training data was found nearest to the local project. But is this the best approach? This paper introduces the Peters filter which is based on the following conjecture: When local data is scarce, more information exists in other projects. Accordingly, this filter selects training data via the structure of other projects. To assess the performance of the Peters filter, we compare it with two other approaches for quality prediction. Within-company learning and cross-company learning with the Burak filter (the state-of-the-art relevancy filter). This paper finds that: 1) within-company predictors are weak for small data-sets; 2) the Peters filter+cross-company builds better predictors than both within-company and the Burak filter+cross-company; and 3) the Peters filter builds 64% more useful predictors than both within-company and the Burak filter+cross-company approaches. Hence, we recommend the Peters filter for cross-company learning.

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Better cross company defect prediction

Software fault prediction aims to identify fault-prone software modules by using some underlying properties of the software project before the actual testing process begins It helps in obtaining d

A study on software fault prediction techniques

When projects lack sufficient local data to make predictions, they try to transfer information from other projects. How can we best support this process? In the field of software engineering, transfer learning has been shown to be effective for defect prediction. This paper checks whether it is possible to build transfer learners for software effort estimation. We use data on 154 projects from 2 sources to investigate transfer learning between different time intervals and 195 projects from 51 sources to provide evidence on the value of transfer learning for traditional cross-company learning problems. We find that the same transfer learning method can be useful for transfer effort estimation results for the cross-company learning problem and the cross-time learning problem. It is misguided to think that: (1) Old data of an organization is irrelevant to current context or (2) data of another organization cannot be used for local solutions. Transfer learning is a promising research direction that transfers relevant cross data between time intervals and domains.

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Transfer learning in effort estimation

Over the past 15years, researchers presented numerous techniques and tools for mining code smells. It is imperative to classify, compare, and evaluate existing techniques and tools used for the detection of code smells because of their varying features and outcomes. This paper presents an up-to-date review on the state-of-the-art techniques and tools used for mining code smells from the source code of different software applications. We classify selected code smell detection techniques and tools based on their detection methods and analyze the results of the selected techniques. We present our observations and recommendations after our critical analysis of existing code smell techniques and tools. Our recommendations may be used by existing and new tool developers working in the field of code smell detection. The scope of this review is limited to research publications in the area of code smells that focus on detection of code smells as compared with previous reviews that cover all aspects of code smells. Copyright © 2015 John Wiley & Sons, Ltd.

A review of code smell mining techniques

There has been a recent surge in interest in the application of Artificial Intelligence (AI) techniques to Software Engineering (SE) problems. The work is typified by recent advances in Search Based Software Engineering, but also by long established work in Probabilistic reasoning and machine learning for Software Engineering. This paper explores some of the relationships between these strands of closely related work, arguing that they have much in common and sets out some future challenges in the area of AI for SE.

The role of artificial intelligence in software engineering

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On software engineering repositories and their open problems

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Harrison

Papers

On software engineering repositories and their open problems

On the statistical distribution of object-oriented system properties