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REFERENCES [1] Erin Kenneally. Gatekeeping Out Of The Box: Open Source Software As A Mechanism To Assess Reliability For Digital Evidence. Virginia Journal of Law and Technology. Vol 6, Issue 3, Fall 2001. [2] Michael W. Andrew “Defining a Process Model for Forensic Analysis of Digital Devices and Storage Media” Proceedings of the 2nd International Workshop on Systematic Approaches to Digital Forensic Engineering SADFE 2007 [3] osi, J., Baa, M. Do we have a full control over integrity in digital evidence life cycle, Proceedings of ITI 2010, 32nd International Conference on Information Technology Interfaces, Dubrovnik/Cavtat, pp. 429-434, 2010 [4] COHEN, M.; GARFINKEL, S.; SCHATZ, B. Extending the advanced forensic format to accommodate multiple data sources, logical evidence, arbitrary information and forensic workflow. Digital Investigation, 2009. S57-S. [5] Turner, P. Unification of Digital Evidence from Disparate Sources (Digital Evidence Bags). In 5th DFRW. 2004. New Orleans [6] Judges’ Awareness, Understanding and Application of Digital Evidence, Phd Thesis in computer technology in Education, Graduate school of computer and information sciences, Nova Southeastern University, 2010 [7] RDF: Model and Syntax Specification. W3C recommendation, 22 Februrary 1999, www.w3.org/TR/REC-rdf-syntax-19990222/1999 [8] The semantic web, Linked and Open Data, A Briefing paper By Lorna M. Campbell and Sheilla MacNeill, June 2010, JISC CETIS [9] Christian Bizer, Tom Heath, Tim Berners-Lee: Linked Data - The Story So Far. Int. J. Semantic Web Inf. Syst. 5(3): 1-22 (2009) [10] Olaf Hartig: Provenance Information in the Web of Data. In Proceedings of the Linked Data on the Web (LDOW) Workshop at the World Wide Web Conference (WWW), Madrid, Spain, Apr. 2009 [11] Olaf Hartig and Jun Zhao: Publishing and Consuming Provenance Metadata on the Web of Linked Data. In Proceedings of the 3rd International Provenance and Annotation Workshop (IPAW), Troy, New York, USA, June 2010 [12] CDESF. Common Digital Evidence Format. 2004 [Viewed 21 December 2005]; Available from: http://www.dfrws.org/CDESF/index.html [13] [Olaf Hartig and Jun Zhao: Using Web Data Provenance for Quality Assessment. In Proceedings of the 1st Int. Workshop on the Role of Semantic Web in Provenance Management (SWPM) at ISWC, Washington, DC, USA, October 2009 Download PDF [14] Olaf Hartig, Jun Zhao, and Hannes Mühleisen: Automatic Integration of Metadata into the Web of Linked Data (Demonstration Proposal). In Proceedings of the 2nd Workshop on Trust and Privacy on the Social and Semantic Web (SPOT) at ESWC, Heraklion, Greece, May 2010 [15] Solvinghe digital Chain of Custody Problem, SPYRUS, Trusted Mobility Solutions, © Copyright 2010 [16] M. I. Cohen, Simson Garfinkel and Bradley Schatz, Extending the Advanced Forensic Format to accommodate Multiple Data Sources, Logical Evidence, Arbitrary Information and Forensic Workflow, DFRWS 2009, Montreal, Canada [17] MD Köhn, JHP Eloff and MS Olivier, "UML Modeling of Digital Forensic Process Models (DFPMs)," in HS Venter, MM Eloff, JHP Eloff and L Labuschagne (eds), Proceedings of the ISSA 2008 Innovative Minds Conference, Johannesburg, South Africa, July 2008 (Published electronically) [18] http://triplify.org/Overview [19] http://dublincore.org/ [20] http://www.foaf-project.org/ [21] P. P. da Silva, D. L. McGuinness, and R. Fikes. A Proof Markup Language for Semantic Web Services. Information Systems, 31(4- 5):381–395, June 2006 [22] osi, J., Baa, M. (2010) A Framework to (Im)Prove Chain of Custody in Digital Investigation Process, Proceedings of the 21st Central European Conference on Information and Intelligent Systems, pp. 435- 438, Varaždin, Croatia [23] osi, J., Baa, M. (2010) (Im)proving chain of custody and digital evidence integrity with timestamp, MIPRO, 33. meunarodni skup za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku, Opatija, 171-175 [24] Schatz, B., Mohay, G. And Clark, A. (2004) ‘Rich Event Representation for computer Forensics’, Proceedings of the 2004 Asia Pacific Industrial Engineering and Management System [25] Schatz,B., Mohay, G. and Clark, A.(2004) ‘Generalising Event Forensics Across Multiple domains’ Proceedings of the 2004 Australian Computer Network and Information Forensics Conference (ACNIFC 2004), Perth, Australia. [26] Bogen, A. and D.Dampier. Knoweldge discovery and experince modeling in computer forensics media analysis.In international Symposium on Information and Communication Technologies.2004: Trinity College Dublin [27] Garfinkel, S.L., D.J. Malan, K.-A. Dubec, C.C. Stevens, and C. Pham, Disk Imaging with the Advanced Forensics Format, library and Tools. Advances in Digital Forensics (2nd Annual IFIP WG 11.9 International Conference on Digital Forensics), 2006 [28] http://www4.wiwiss.fu-berlin.de/bizer/pub/linkeddatatutorial/ 411
A Holistic Approach to Software Traceability Hazeline U. Asuncion Computing and Software Systems University of Washington, Bothell Bothell, WA 98011 USA hazeline@u.washington.edu Richard N. Taylor Institute for Software Research University of California, Irvine Irvine, CA 92697 USA taylor@ics.uci.edu Abstract—Software traceability is recognized for its utility in many software development activities. Nonetheless, applying traceability in practice is generally difficult due to interrelated factors such as high overhead costs, the presence of heterogeneous artifacts and tools, and low user motivation. In addition, many traceability techniques tend to only address difficulties from either the economic, technical, or social perspective, but not all three. To holistically tackle these three dimensions, we created a traceability technique, Architecture- Centric Traceability for Stakeholders (ACTS). The ACTS technique connects distributed and varied artifacts around concepts represented by the architecture, enables stakeholders to trace to artifacts that are of interest to them, and captures traceability links prospectively in the background as stakeholders perform development tasks. Our case studies of the ACTS tool usage reveal that it is possible to simultaneously address economic, technical, and social difficulties, suggesting that such an approach can aid the adoption of traceability in industry. Keywords-documentation; software traceability; software architecture; open hypermedia I. INTRODUCTION In a typical softwa re development setting, num erous artifacts are produced throughout the s oftware lifecycle. Software traceability aims to identify and explicitly link together related artifacts to support various softw are engineering tasks, and is useful in system comprehension, impact analysis, and system debugging [18, 26]. Despite these benefits, implementing novel traceability methods within an industry setting is often very difficult [20]. Manual approaches are tedious and error-prone; as a result, software engineers have an aversion to traceability tasks [18]. Autom ated techniques often require human intervention [15]. We posit that the difficulties with transitioning traceability techniques to real-world settings stem from a na rrow understanding of the challenges around tra ceability. In fact, many interacting factors hinder traceability, such as high c osts [18], complex interrelationships between artifacts [2], heterogeneity of artifacts and tools [14], and varied stakeholder interests [13, 29]. These factors, which reflect economic, technical, and social perspectives, must all be addressed to realize the benefits of traceability. This material is based in part upon work supported by the National Science Foundation under Grant No. 0917129. This paper presents a novel technique to traceability that aims to holistically address interacting challenges from these three perspectives. Our technique, Architecture-Centric Traceability for Stakeholders (ACTS) adva nces the state-ofthe-art in traceability by (1) connecting distributed and varied artifacts to concepts re presented by the architecture, (2) enabling stakeholders to trace to artifacts that are of intere st to them, and (3) capturing traceability prospectively, in situ, as software engineers perform development tasks. In previous work, we discussed traceability challenges from the three perspectives within the specializ ed context of one organization [6]. ACTS provides a generalized and automated technique for holistically addressing traceability challen ges, using open hypermedia and rules to generate trace links. More recently, we com bined architecture-centric traceability with a machine learning m ethod known as topic m odeling [5]; however, that work did not fo cus on holistic traceability. In this work, we focus on the rationale and methodology behind ACTS and perform case studies that suggest that our a pproach has utility from economic, technical, and social perspectives. In the next section, we discuss the importance of analyzing the challenges to traceability from the three perspectives and survey existing techniques. Section 3 presents the main elements of ACTS. Section 4 covers the A CTS tool support. Section 5 dis cusses three case studies of tool usage, and Section 6 concludes with future work. II. PROBLEM ANALYSIS AND EXISTING TECHNIQUES Traceability challenges can be viewed from economic, technical, and social perspectives, which we describe below. A. Economic Perspective The economic perspective focuses on the cost of supporting traceability. Costs can be in term s of labor hours [18], documentation, and user training time [6]. High cost is one of the major hindering factors to traceability [18]. Technique s to minimize cost include examining the trade offs between cost and quality or between cost and benefit [12]. B. Technical Perspective The challenges from the technical perspective include the complexity of tracing due to the heterogeneity of artifacts and tools [14, 21], the combinatorial explosion of the artifact space [20], and the continuous and independent evolution of artifacts. There are several approac hes that tackle these technical challenges. Techniques to address the heterogeneity of artifacts include natural language proce ssing [8], model transformation [19], and translation techniques [2]. Techniques to address the heterogeneity of artifacts and tools include 412
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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80
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82
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84
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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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484
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A Tiny Specification Metalanguage W
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C. Syntax Extensions The expressive
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however, does not allow variables f
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Suppose P = 〈S P , SP i , AI P ,
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
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III. VALIDATING THE DIMENSION HEADE
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The table title and the dimension s
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A light weight alternative for OLAP
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i.d. subsets of cubes focused on se
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Match production rules Enterprise S
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A Tool for Visualization of a Knowl
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other ontology visualization tools
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shown at Figure 5. Objectives are s
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Rendering UML Activity Diagrams as
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a = O1 → a → O2 b = O2 → b
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ecordProblem → A → reproducePro
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umlTUowl - A Both Generic and Vendo
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The tool’s ability to deal with S
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Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
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III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
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742
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744
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746
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her necessities. However, the progr
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Figure 3. Global Log. of terms. The
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two stages. The first stage is focu
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754
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756
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758
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
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764
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766
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Proactive Two Way Mobile Advertisem
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information. If more than one adver
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Users can al so publish adv ertisem
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The COIN Platform: Supporting the M
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A-3
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Checking Contracts for AOP using XP
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Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
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Wei Zhang, 422 Wenbo Zhang, 188 Zhi
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Desmond Greer Eric Gregoire Christi
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Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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