Throughout the years, empirical studies have found power law distributions in various measures across many software systems. However, surprisingly little is known about how they are produced. What causes these power law distributions? We offer an explanation from the perspective of fine-grained code changes. A model based on preferential attachment and self-organized criticality is proposed to simulate software evolution. The experiment shows that the simulation is able to render power law distributions out of fine-grained code changes, suggesting preferential attachment and self-organized criticality are the underlying mechanism causing the power law distributions in software systems.

While many bug prediction algorithms have been developed by academia, they're often only tested and verified in the lab using automated means. We do not have a strong idea about whether such algorithms are useful to guide human developers. We deployed a bug prediction algorithm across Google, and found no identifiable change in developer behavior. Using our experience, we provide several characteristics that bug prediction algorithms need to meet in order to be accepted by human developers and truly change how developers evaluate their code.

Simulations have been used in various areas, yielding good results, but their application to software evolution is still limited. Simulations of software evolution can help people understand the driving forces that shape software evolution, and predict future evolutionary paths. To move towards simulation of software evolution, this research tries to explore possible models to simulate software evolution, and the applicability of different data to parameterize the models. The simulations will both be based on fine-grained code changes obtained by comparing the abstract syntax trees of source code. The use of fine-grain code changes could reveal information about software evolution that is unavailable by other means.

The FixCache algorithm, introduced in 2007, effectively identifies files or methods which are likely to contain bugs by analyzing source control repository history. However, many open questions remain about the behaviour of this algorithm. What is the variation in the hit rate over time? How long do files stay in the cache? Do buggy files tend to stay buggy, or can they be redeemed? This paper analyzes the behaviour of the FixCache algorithm on four open source projects. FixCache hit rate is found to generally increase over time for three of the four projects; file duration in cache follows a Zipf distribution; and topmost bug-fixed files go through periods of greater and lesser stability over a project's history.

Nowadays, commercial or in-house customized process management tools have been prevalently adopted for supporting software project management and process improvement. In this paper we report a case study to empirically investigate and evaluate the usage status and implications of a supporting tool named QONE in industrial environment in China. Decision theory is adopted in study design. The analysis is mainly based on the usage data from a typical industrial project. Further questionnaires and follow-up interviews with the end-users are also conducted. The analysis results reveal that 1) the effects of such supporting tool vary with respect to different task types, 2) tasks with smaller granularity are comparatively easier to predict and control, 3) missing data reporting analysis helps to reveal opportunities for further process improvement and tool enhancement. This investigation aims to help us take advantages of such supporting tools and benefit software development eventually.

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.