Âê̾ | ¥â¡¼¥·¥ç¥ó¤Ë¤è¤ë¥Þ¥¦¥¹Áàºî¤Î¼Â¸½ |
Ãø¼Ô | *Æ£°æ ·òÂÀ, ËÌÀî ʸÉ× (²¬»³Íý²ÊÂç³Ø) |
Page | pp. 403 - 404 |
Keyword | ¥â¡¼¥·¥ç¥ó¥¥ã¥×¥Á¥ã, Kinect, ¥Þ¥¦¥¹ |
Abstract | ¤³¤³¿ôǯ¤Î¥²¡¼¥à¥³¥ó¥È¥í¡¼¥é¤Îµ¡Ç½¤Î¿Ê²½¤ÏÃø¤·¤¯¡¢´Êñ¤Ê¥â¡¼¥·¥ç¥ó¥¥ã¥×¥Á¥ã¡¼¤ò¹Ô¤¨¤ë¤Þ¤Ç¤Ë¤Ê¤Ã¤Æ¤¤¤ë¡£¤Ê¤«¤Ç¤âMicrosoft¤ÎKinect¤Ï¤¢¤ëÄøÅÙ¤ÎÀºÅÙ¤ò»ý¤Ã¤¿¾ðÊ󤬼èÆÀ¤Ç¤¤ë¡£¤³¤ÎÊó¹ð¤Ç¤Ï¡¢Kinect¤ò»È¤¤¥â¡¼¥·¥ç¥ó¤ò¿ô¼ïÎà¶èÊ̤¹¤ë¤³¤È¤Ç¡¢¥Þ¥¦¥¹¤ÎÁàºî¤ËÂбþ¤·¤¿¥½¥Õ¥È¥¦¥§¥¢¤Î¼Â¸½¤Ë¤Ä¤¤¤Æ¤ªÏä·¤¹¤ë¡£ |
Âê̾ | ²Ã®ÅÙ¥»¥ó¥µ¤òÍѤ¤¤¿¥¿¥Ã¥Á¥Ñ¥Í¥ëÁàºî»þ¤Î±¿Æ°Ç½ÎÏɾ²Á |
Ãø¼Ô | *´äùõ Í¥, ×¢ÉÚ Å¯Ìé (Å纬Âç³Ø) |
Page | p. 405 |
Keyword | ²Ã®ÅÙ¥»¥ó¥µ, ¥¿¥Ã¥Á¥Ñ¥Í¥ëÁàºî, ±¿Æ°Ç½ÎÏ, ÉÔ¿ï°Õ±¿Æ°, ¥·¡¼¥Æ¥£¥ó¥° |
Âê̾ | A Study on Analogy-based Multiple Regression Model for Software Effort Estimation |
Ãø¼Ô | *Xiaohan Ban, Xiao Xiao, Tadashi Dohi (Hiroshima University) |
Page | p. 406 |
Keyword | Software effort estimation, multiple regression, analogy |
Abstract | Software effort estimation is an important issue in the achievement of a successful software project management. Dejaeger et. al showed that multiple regression (MR) model in combination with a logarithmic transformation performed best. In this paper, we propose an analogy-based MR model with the idea of analogy, and compare the estimation accuracy of the proposed and conventional methods. According to experimental results, it can be concluded that our model is able to perform better than conventional MR model. |
Âê̾ | Optimal Allocation of Software Testing-Resources with Reliability Criterion |
Ãø¼Ô | *Bing Wu, Xiao Xiao, Tadashi Dohi (¹ÅçÂç³Ø) |
Page | p. 407 |
Keyword | testing-rsources, reliability criterion, reliability, optimal allocation |
Abstract | For a software development process, 40%~50% of the total software development resources are consumed in the testing phase. Therefore the software testing resource allocation is one of the most significant issues in software project management. Ukimoto et al. formulated three optimization problems for the software testing-resource allocation. In this paper, we take software reliability into account and formulate three new optimization problems by considering a desired reliability objective. We solve these problems by the Lagrange multiplier method. |
Âê̾ | Development of Software Reliability Models with Kernel Methods |
Ãø¼Ô | *Kei Okumura, Hiroyuki Okamura, Tadashi Dohi (¹ÅçÂç³ØÂç³Ø±¡¹©³Ø¸¦µæ²Ê¾ðÊ󹩳ØÀ칶) |
Page | p. 408 |
Keyword | Software reliability growth model, Software metrics, Poisson regression, Kernel method |
Abstract | This paper presents a software reliability growth model (SRGM) using kernel methods. In recent years, several papers have presented SRGMs which deal with software metrics. However these models require us to select a set of software metrics before evaluating the software reliability. In general, it is difficult to determine the best set of software metrics to estimate the reliability, but it strongly affects the predictive performance of metrics-based SRGMs. In this paper, by using kernel methods that map into a high dimensional feature space, we develop an SRGM without software metrics selection. |
Âê̾ | ¶ËÃÍʬÉۤ˴ð¤Å¤¤¤¿2¹à¥½¥Õ¥È¥¦¥§¥¢¿®ÍêÀ¥â¥Ç¥ë |
Ãø¼Ô | *¾Ó ð¼, ÅÚÈî Àµ (¹ÅçÂç³Ø) |
Page | p. 409 |
Keyword | ¥½¥Õ¥È¥¦¥§¥¢¿®ÍêÀ, 2¹à¥â¥Ç¥ë, ¶ËÃÍʬÉÛ, NHPP, ͽ¬ɾ²Á |
Abstract | 2 ¹à²áÄø (BP) ¤Ë´ð¤Å¤¤¤¿¥½¥Õ¥È¥¦¥§¥¢¿®ÍêÀ¥â¥Ç¥ë (SRM) ¤Ï¥Æ¥¹¥ÈÃʳ¬¤Ë¤ª¤¤¤Æ¸¡½Ð¤µ¤ì¤ë¥½¥Õ¥È¥¦¥§¥¢¥Õ¥©¡¼¥ë¥È¿ô¤Î»þ´ÖŪµóÆ°¤òµ½Ò¤¹¤ë¤¿¤á¤Î³ÎΨ¥â¥Ç¥ë¤Ç¤¢¤ê¡¤¥Õ¥©¡¼¥ë¥È¸¡½Ð»þ´ÖʬÉÛ¤ËÍÍ¡¹¤Ê³ÎΨʬÉÛ¤ò²¾Äꤹ¤ë¤³¤È¤Ç¡¤Â¿¤¯¤Î¥Õ¥©¡¼¥ë¥È¸¡½Ð¥Ñ¥¿¡¼¥ó¤òɽ¸½¤¹¤ë¤³¤È¤¬²Äǽ¤Ç¤¢¤ë¡¥°ìÊý¡¤¶ËÃÍʬÉÛ (EVD) ¤Ï¡¤ÆÈΩ¤ÇƱ°ì¤ÊʬÉۤ˽¾¤¦Ê£¿ô¤Î³ÎΨÊÑ¿ô¤ÎºÇÂçÃͤäºÇ¾®Ãͤ¬Á²¶áŪ¤Ë½¾¤¦³ÎΨʬÉۤǤ¢¤ê¡¤¿®ÍêÀʬÌî¤Ç¤Ï¡¤ÊÂÎó¹½Â¤Ëô¤ÏľÎó¹½Â¤¤ò»ý¤Ä¥·¥¹¥Æ¥à¤Î¿®ÍêÀɾ²Á¤Ë¤è¤¯ÍѤ¤¤é¤ì¤ë¡¥Àè¹Ô¸¦µæ¤Ç¤Ï BP-based SRM ¤Ë°¤¹¤ëÈóƱ¼¡¥Ý¥¢¥½¥ó²áÄø¤Ë´ð¤Å¤¤¤¿¥½¥Õ¥È¥¦¥§¥¢¿®ÍêÀ¥â¥Ç¥ë (NHPP-based SRM) ¤Ë EVD ¤òŬÍѤ·¡¤Å¬¹çÀ¤äͽ¬Àǽ¤Î´ÑÅÀ¤«¤é¥½¥Õ¥È¥¦¥§¥¢¿®ÍêÀɾ²Á¤Ë¤ª¤±¤ë EVD ¤ÎÍÍÑÀ¤ò¸¡¾Ú¤·¤¿¡¥ËܹƤǤϡ¤EVD ¤ò BP-based SRM ¤Î¥Õ¥©¡¼¥ë¥È¸¡½Ð»þ´ÖʬÉۤȲ¾Äꤷ¡¤EVD-BP-based SRM ¤òÄó°Æ¤¹¤ë¡¥ |