Bug assignment is an important step in bug life-cycle management. In large projects, this task would consume a substantial amount of human effort. To compare with the previous studies on automatic bug assignment in FOSS (free/open source software) projects, we conduct a case study on a proprietary software project in China. Our study consists of two experiments of automatic bug assignment, using Chinese text and the other non-text information of bug data respectively. Based on text data of the bug repository, the first experiment uses SVM to predict bug assignments and achieve accuracy close to that by human triagers. The second one explores the usefulness of non-text data in making such prediction. The main results from our study includes that text data are most useful data in the bug tracking system to triage bugs, and automation based on text data could effectively reduce the manual effort.

Data transformation and attribute subset selection have been adopted in improving software defect/failure prediction methods. However, little consensus was achieved on their effectiveness. This paper reports a comparative study on these two kinds of techniques combined with four classifier and datasets from two projects. The results indicate that data transformation displays un obvious influence on improving the performance, while attribute subset selection methods show distinguishably inconsistent output. Besides, consistency across releases and discrepancy between the open-source and in-house maintenance projects in the evaluation of these methods are discussed.

Productivity is a critical performance index of process resources. As successive history productivity data tends to be auto-correlated, time series prediction method based on auto-regressive integrated moving average (ARIMA) model was introduced into software productivity prediction by Humphrey et al. In this paper, a variant of their prediction method named ARIMAmmse is proposed. This variant formulates the ARIMA parameter estimation issue as a minimum mean square error (MMSE) based constrained optimization problem. The ARIMA model is used to describe constraints of the parameter estimation problem, while MMSE is used as the objective function of the constrained optimization problem. According to the optimization theory, ARIMAmmse will definitely achieve a higher MMSE prediction precision than Humphrey et al's which is based on the Yule-Walk estimation technique. Two comparative experiments are also presented. The experimental results further confirm the theoretical superiority of ARIMAmmse

XP (eXtreme Programming) has become popular for IID (Iteration and Increment Development). It is suitable for small teams, lightweight projects and vague/volatile requirements. However, some challenges are left to developers when they desire to practise XP. A critical one of them is constructing the release plan and negotiating it with customers. In this paper, we propose a risk-driven method for XP release planning. It has been applied in a case study and the results show the method is feasible and effective. XP practicers can follow it to decide a suitable release plan and control the development process.