Data mining for software engineering and humans in the loop

Abstract

The field of data mining for software engineering has been growing over the last decade. This field is concerned with the use of data mining to provide useful insights into how to improve software engineering processes and software itself, supporting decision-making. For that, data produced by software engineering processes and products during and after software development are used. Despite promising results, there is frequently a lack of discussion on the role of software engineering practitioners amidst the data mining approaches. This makes adoption of data mining by software engineering practitioners difficult. Moreover, the fact that experts' knowledge is frequently ignored by data mining approaches, together with the lack of transparency of such approaches, can hinder the acceptability of data mining by software engineering practitioners. To overcome these problems, this position paper provides a discussion of the role of software engineering experts when adopting data mining approaches. It also argues that this role can be extended to increase experts' involvement in the process of building data mining models. We believe that such extended involvement is not only likely to increase software engineers' acceptability of the resulting models, but also improve the models B Leandro L. Minku themselves. We also provide some recommendations aimed at increasing the success of experts involvement and model acceptability.

Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

“…DCL could also assist project managers with project planning (e.g., what team expertise to use), and requirement elicitators with which requirements to implement (e.g., whether it is worth lifting or including extra memory or CPU constraints, etc. ), as suggested by the work of Minku et al (2015). This is because practitioners could get SEEs for different sets of input attribute values for a project, gaining insights into how much the resulting effort would vary.…”

Application of data mining methods for effort estimation of software projects

“…This is the result of both the increased availability of data and computing power and the increased utilization of data science and analytics . Data mining is used to provide useful insights into how to improve the software engineering process and software itself …”

“…Effort estimation represents a serious challenge, and despite numerous efforts to produce accurate estimates, judgment‐based estimation remains the most widely used technique in practice . However, if it is possible, it is suggested to use some other source or technique as a support to improve the quality of effort estimation …”

“…3 Data mining is used to provide useful insights into how to improve the software engineering process and software itself. 4 Effort estimation is an important part of software project management and is performed in a majority of projects. 5 Effort estimation represents a serious challenge, and despite numerous efforts to produce accurate estimates, judgment-based estimation remains the most widely used technique in practice.…”

“…6 However, if it is possible, it is suggested to use some other source or technique as a support to improve the quality of effort estimation. 4 With the goal of increasing the reliability of experts' effort estimation, this paper presents usage of the knowledge discovery approach. It uses data mining methods in combination with machine learning techniques to build a predictive model and produce the required effort estimation.…”