{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T04:04:56Z","timestamp":1759032296399,"version":"3.41.0"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2009,10,1]],"date-time":"2009-10-01T00:00:00Z","timestamp":1254355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"LNCSR","award":["22136"],"award-info":[{"award-number":["22136"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCR-0098325CCR-0702693"],"award-info":[{"award-number":["CCR-0098325CCR-0702693"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2009,10]]},"abstract":"<jats:p>\n            <jats:italic>Dynamic information flow analysis<\/jats:italic>\n            (DIFA) was devised to enable the flow of information among variables in an executing program to be monitored and possibly regulated. It is related to techniques like\n            <jats:italic>dynamic slicing<\/jats:italic>\n            and\n            <jats:italic>dynamic impact analysis<\/jats:italic>\n            . To better understand the basis for DIFA, we conducted an empirical study in which we measured the\n            <jats:italic>strength<\/jats:italic>\n            of information flows identified by DIFA, using information theoretic and correlation-based methods. The results indicate that in most cases the occurrence of a chain of dynamic program dependences between two variables does\n            <jats:italic>not<\/jats:italic>\n            indicate a measurable information flow between them. We also explored the relationship between the strength of an information flow and the\n            <jats:italic>length<\/jats:italic>\n            of the corresponding dependence chain, and we obtained results indicating that no consistent relationship exists between the length of an information flow and its strength. Finally, we investigated whether data dependence and control dependence makes equal or unequal contributions to flow strength. The results indicate that flows due to data dependences alone are stronger, on average, than flows due to control dependences alone. We present the details of our study and consider the implications of the results for applications of DIFA and related techniques.\n          <\/jats:p>","DOI":"10.1145\/1571629.1571631","type":"journal-article","created":{"date-parts":[[2009,10,9]],"date-time":"2009-10-09T19:06:16Z","timestamp":1255115176000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Measuring the strength of information flows in programs"],"prefix":"10.1145","volume":"19","author":[{"given":"Wes","family":"Masri","sequence":"first","affiliation":[{"name":"American University of Beirut, Beirut, Lebanon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andy","family":"Podgurski","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2009,10,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/93548.93576"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1062455.1062534"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2004.78"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/579090"},{"key":"e_1_2_1_5_1","unstructured":"Box G. E. P. Hunter J. S. and Hunter W. G. 2005. Statistics for Experimenters: Design Innovation and Discovery 2nd ed. Wiley New York NY.  Box G. E. P. Hunter J. S. and Hunter W. G. 2005. Statistics for Experimenters: Design Innovation and Discovery 2nd ed. Wiley New York NY."},{"volume-title":"Proceedings of Quantitative Aspects of Programming Languages (QAPL).","author":"Clark D.","key":"e_1_2_1_6_1"},{"volume-title":"Proceedings of the IFIP WG 1.7, ACM SIGPLAN, and GI FoMSESS Workshop on Issues in the Theory of Security","author":"Clark D.","key":"e_1_2_1_7_1"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273463.1273490"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Cover T. M. and Joy T. A. 1991. Elements of Information Theory. Wiley New York NY.   Cover T. M. and Joy T. A. 1991. Elements of Information Theory. 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Regression on Multiple Categories. visualstatistics.net."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/362375.362389"},{"volume-title":"Proceedings of the International Conference on Software Engineering. 308--318","author":"Law J.","key":"e_1_2_1_21_1"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/998675.999392"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/794201.795167"},{"key":"e_1_2_1_24_1","unstructured":"Masri W. 2004. Dynamic information flow analysis slicing and profiling. Ph.D. dissertation. Case Western Reserve University Cleveland OH. wwwlib.umi.com\/dissertations\/fullcit\/3146448.   Masri W. 2004. Dynamic information flow analysis slicing and profiling. Ph.D. dissertation. Case Western Reserve University Cleveland OH. wwwlib.umi.com\/dissertations\/fullcit\/3146448."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1083200.1083216"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1138912.1138927"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE.2004.17"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2007.1020"},{"key":"e_1_2_1_29_1","unstructured":"McCamant S. and Ernst M. 2006. Quantitative information-flow tracking for C and related languages. MIT Computer Science and Artificial Intelligence Laboratory Tech. rep. MIT-CSAIL-TR-2006-076. MIT Cambridge MA.  McCamant S. and Ernst M. 2006. Quantitative information-flow tracking for C and related languages. MIT Computer Science and Artificial Intelligence Laboratory Tech. rep. MIT-CSAIL-TR-2006-076. 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