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020 ▼a 9780438206472
035 ▼a (MiAaPQ)AAI10785980
035 ▼a (MiAaPQ)rpi:11259
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Hathaway, Charles.
24510 ▼a Broadening the Applicability of Software Complexity Metrics with Biological Analogues.
260 ▼a [S.l.] : ▼b Rensselaer Polytechnic Institute., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 149 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: Ron Eglash
5021 ▼a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
520 ▼a Software metrics represent an application of conventional computer science to itself in an attempt to quantify the complexity of software systems. Many of the conventional metrics proposed in academia and industry find their roots in a top-down
520 ▼a Chapters 5 and 6 compare taint analysis, measured using standard complexity metrics, with an ecosystem approach that examines interactive complexity. Chapter 3 examines the use of traditional complexity metrics to measure student comprehension o
520 ▼a Combining these bio-inspired and traditional metrics utilizing machine learning techniques, also inspired by biology and neurology, chapter 7 demonstrates that greater results can be achieved with both angles than a single perspective.
590 ▼a School code: 0185.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a Rensselaer Polytechnic Institute. ▼b Computer Science.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0185
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997334 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자