Studying re-opened bugs in open source software

Bug fixing accounts for a large amount of the software maintenance resources. Generally, bugs are reported, fixed, verified and closed. However, in some cases bugs have to be re-opened. Re-opened bugs increase maintenance costs, degrade the overall user-perceived quality of the software and lead to unnecessary rework by busy practitioners. In this paper, we study and predict re-opened bugs through a case study on three large open source projects—namely Eclipse, Apache and OpenOffice. We structure our study along four dimensions: (1) the work habits dimension (e.g., the weekday on which the bug was initially closed), (2) the bug report dimension (e.g., the component in which the bug was found) (3) the bug fix dimension (e.g., the amount of time it took to perform the initial fix) and (4) the team dimension (e.g., the experience of the bug fixer). We build decision trees using the aforementioned factors that aim to predict re-opened bugs. We perform top node analysis to determine which factors are the most important indicators of whether or not a bug will be re-opened. Our study shows that the comment text and last status of the bug when it is initially closed are the most important factors related to whether or not a bug will be re-opened. Using a combination of these dimensions, we can build explainable prediction models that can achieve a precision between 52.1–78.6 % and a recall in the range of 70.5–94.1 % when predicting whether a bug will be re-opened. We find that the factors that best indicate which bugs might be re-opened vary based on the project. The comment text is the most important factor for the Eclipse and OpenOffice projects, while the last status is the most important one for Apache. These factors should be closely examined in order to reduce maintenance cost due to re-opened bugs.     

RUNA: a multimodal command language for home robot users

This article describes a multimodal command language for home robot users, and a robot system which interprets users’ messages in the language through microphones, visual and tactile sensors, and control buttons. The command language comprises a set of grammar rules, a lexicon, and nonverbal events detected in hand gestures, readings of tactile sensors attached to the robots, and buttons on the controllers in the users’ hands. Prototype humanoid systems which immediately execute commands in the language are also presented, along with preliminary experiments of faceto-face interactions and teleoperations. Subjects unfamiliar with the language were able to command humanoids and complete their tasks with brief documents at hand, given a short demonstration beforehand. The command understanding system operating on PCs responded to multimodal commands without significant delay. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Success rates in a multimodal command language for home robot users

This article considers the success rates in a multimodal command language for home robot users. In the command language, the user specifies action types and action parameter values to direct robots in multiple modes such as speech, touch, and gesture. The success rates of commands in the language can be estimated by user evaluations in several ways. This article presents some user evaluation methods, as well as results from recent studies on command success rates. The results show that the language enables users without much training to command home robots at success rates as high as 88%–100%. It is also shown that multimodal commands combining speech and button-press actions included fewer words and were significantly more successful than single-modal spoken commands.     

Negative emotion recognition using multimodal physiological signals for advanced driver assistance systems

Artificial Life and Robotics - Recent advanced driver assistance systems’ (ADASs) control cars to avoid accidents, but few of them consider driver’s comfort. To realize comfortable...     

A Possible Magnetic Phase with Scalar Chirality in Solid 3He Layers

We study quantum effects in a system of S = 1/2 spins with two–, three and four–spin exchange interactions on the triangular lattice, a possible model of the nuclear magnetism of solid ³ He layers. The ground state phases found in our previous work are studied by using the linear spin–wave theory. The uuud state shows a very flat spin–wave dispersion, which suggests that this state, though locally stable, will not survive non–linear quantum effects. The tetrahedral state, which has long–range order of a scalar chirality, is found to be stable against spin wave fluctuations. The ground state energies of different phases are compared. Results of numerical analysis of finite size clusters are also presented.     

Hybrid Petri net based modeling for biological pathway simulation

Hybrid Petri net (HPN) is an extension of the Petri net formalism, which enables us to handle continuous information in addition to discrete information. Firstly, this paper demonstrates how biological pathways can be modeled by the integration of discrete and continuous elements, with an example of the λ phage genetic switch system including induction and retroregulation mechanisms. Although HPN allows intuitive modeling of biological pathways, some fundamental biological processes such as complex formation cannot be represented with HPN. Thus, this paper next provides the formal definition of hybrid functional Petri net with extension (HFPNe), which has high potential for modeling various kinds of biological processes. Cell Illustrator is a software tool developed on the basis of the definition of HFPNe. Hypothesis creation by Cell Illustrator is demonstrated with the example of the cyanobacterial circadian gene clock system. Finally, our ongoing tasks, which include the development of a computational platform for systems biology, are presented.     

Tabu search for attribute reduction in rough set theory

In this paper, we consider a memory-based heuristic of tabu search to solve the attribute reduction problem in rough set theory. The proposed method, called tabu search attribute reduction (TSAR), is a high-level TS with long-term memory. Therefore, TSAR invokes diversification and intensification search schemes besides the TS neighborhood search methodology. TSAR shows promising and competitive performance compared with some other CI tools in terms of solution qualities. Moreover, TSAR shows a superior performance in saving the computational costs.