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Vartak, Manasi; Rahman, Sajjadur; Madden, Samuel; Parameswaran, Aditya; Polyzotis, Neoklis

doi:10.14778/2831360.2831371

Cited by 161 publications

(5 citation statements)

References 33 publications

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“…Further, Shneiderman divided EVA into three phases, similar to those we suggest, with his Visual Information Seeking Mantra: "Overview first, zoom and filter, then details-ondemand" [92]. While techniques from this field likely cannot be applied as-is due to differences in the underlying data's nature, these similarities suggest insights from EVA could be leveraged to guide similar development in RE tools, including methods for data exploration [93][94][95][96], interaction [97][98][99][100], and predicting future analysis questions [101][102][103][104].…”

Section: Dream++ Decompiler [5]mentioning

confidence: 70%

An Observational Investigation of Reverse Engineers' Process and Mental Models

Votipka

Rabin

Micinski

et al. 2019

Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

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Reverse engineering is a complex process essential to software-security tasks such as vulnerability discovery and malware analysis. Significant research and engineering effort has gone into developing tools to support reverse engineers. However, little work has been done to understand the way reverse engineers think when analyzing programs, leaving tool developers to make interface design decisions based only on intuition.This paper takes a first step toward a better understanding of reverse engineers' processes, with the goal of producing insights for improving interaction design for reverse engineering tools. We present the results of a semi-structured, observational interview study of reverse engineers (N=16). Each observation investigated the questions reverse engineers ask as they probe a program, how they answer these questions, and the decisions they make throughout the reverse engineering process. From the interview responses, we distill a model of the reverse engineering process, divided into three phases: overview, sub-component scanning, and focused experimentation. Each analysis phase's results feed the next as reverse engineers' mental representations become more concrete. We find that reverse engineers typically use static methods in the first two phases, but dynamic methods in the final phase, with experience playing large, but varying, roles in each phase. Based on these results, we provide five interaction design guidelines for reverse engineering tools.

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Section: Dream++ Decompiler [5]mentioning

confidence: 70%

An Observational Investigation of Reverse Engineers' Process and Mental Models

Votipka

Rabin

Micinski

et al. 2019

Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

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“…Visualization recommender systems also model the visual exploration space and evaluate various measures over the space to decide what to present the user. For instance, Rank-by-Feature [35], AutoVis [43], Voyager [44], SeeDB [41], and Foresight [7] use statistical features and perceptual effectiveness to structure the presentation of possible visualizations of data. Clustrophile 2 also provides methods for enumeration and ranking of visual explorations.…”

Section: Guiding Users In Exploratory Data Analysismentioning

confidence: 99%

Clustrophile 2: Guided Visual Clustering Analysis

Cavallo¹,

Demiralp²

2019

IEEE Trans. Visual. Comput. Graphics

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Data clustering is a common unsupervised learning method frequently used in exploratory data analysis. However, identifying relevant structures in unlabeled, high-dimensional data is nontrivial, requiring iterative experimentation with clustering parameters as well as data features and instances. The number of possible clusterings for a typical dataset is vast, and navigating in this vast space is also challenging. The absence of ground-truth labels makes it impossible to define an optimal solution, thus requiring user judgment to establish what can be considered a satisfiable clustering result. Data scientists need adequate interactive tools to effectively explore and navigate the large clustering space so as to improve the effectiveness of exploratory clustering analysis. We introduce Clustrophile 2, a new interactive tool for guided clustering analysis. Clustrophile 2 guides users in clustering-based exploratory analysis, adapts user feedback to improve user guidance, facilitates the interpretation of clusters, and helps quickly reason about differences between clusterings. To this end, Clustrophile 2 contributes a novel feature, the Clustering Tour, to help users choose clustering parameters and assess the quality of different clustering results in relation to current analysis goals and user expectations. We evaluate Clustrophile 2 through a user study with 12 data scientists, who used our tool to explore and interpret sub-cohorts in a dataset of Parkinson's disease patients. Results suggest that Clustrophile 2 improves the speed and effectiveness of exploratory clustering analysis for both experts and non-experts.

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“…The first generations of visualization tools for the information-seeking are embedded with the basicvisualinterface (Zhou&Feiner,1998),andbeliefontheuser'scognitionforvisualelements (Eppler,2006).Thestudiesin (Goldsmith,1984;Treisman,1982;Belkin,1993;Janiszewski,1998) statesthatahumaniscapableofperceivingcertainvisualproperties:Position, Size, Orientation, Color, or shape, pre-fetchingetc.Thepotentialvisualizationinterfacesdesigned,suchasHiveRel (Yogev,Shani&Tractinsky,2017),HotMaps(Hoeber&Yang,2006,TileBar (Hearst,1995),CNVis (Parra,Brusilovsky&Trattner,2014),InfoSky (Andrews,Kienreich,Sabol&Tochtermann,2002), InfoCrystal (Spoerri,1993),RankSpiral (Spoerri,2004),Citelogy (Matejka,Grossman&Fitzmaurice, 2012), SeeDB (Vartak, Rahman, Madden & Polyzotis, 2015), IntentStream (Andolina, Klouche, Ruotsalo&Jacucci,2015),SIS (Dumais,Cutrell,Cadiz&Robbins,2016),SmallWorld (Gretarsson, O'Donovan,Bostandjiev,Hall&Höllerer,2010),TasteWieght ,LineUp (Gratzl,Lex&Streit,2013),LifeFlow (Ahn,Wongsuphasawat&Brusilovsky,2011), IntentRadar (Ruotsalo,Peltonen,Eugster,Glowacki&Kaski,2014),PivotPath (Dörk,Riche,Ramos &Dumais,2012),Microcosm (Roitman,Raviv,Hummel,Erera&Konopniki,2014),andCognilearn (Gattupalli,Ebert,Papakostas,Makedon&Athitsos,2017).Avisualinterfacepresentsinformation inameta-structurestotheusers,insteadofplaintext,suchasTile Bar, Theme view, Radar view, GraphView,BullyEye view, Slide Bar,etc.arecommonlybuiltinaninterface,andmeta-structures suchasInfoCrystal,Net View,Venn diagram,Coordinated view,Intent StreamandAlluvial diagram. Eachmeta-structureillustratesdiverseaspectsofextracteddataandsuitabletoparticularusertype. Traditionally, a visual interface produces information objects in meta-structure and visual formationbasedontworelevancemeasures:Position-of-Interest (POI) orSet-based(Gelfert,2018).…”

Section: Visual Interfacementioning

confidence: 99%

Visual Task Characterization for Exploratory Search

Singh

2020

International Journal of Information Systems and Social Change

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Humans are living in the age of big data, advanced analytics, and human information interaction. The tremendous growth of ‘the data' presents enormous challenges to information-seeking. A naïve user adapts several strategies (e.g., analytical search, search by analogy, etc.) to aggregate the knowledge. Augmented visualization plays a pivotal role via visual cognitive support in user searches (i.e., simple visual search, visual exploration, etc.). Though it is hard for a user to look for specific tasks or visual metaphors, as evaluation of visual task is rarely undertaken, it is unclear what role, if any, information visualizations play in the user search. In this paper, a visual taxonomy is adopted to systematically review key visualization tools to understand the visual tasks and inherent functions and viability. The key contribution is to trace the current trends and advancements on the dimensions (i.e., visual accomplishments and visual implications) and to assert that annotations will provide a vision on the state of exploratory visual analytics, evolving frameworks, and tools.

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S ee DB

Cited by 161 publications

References 33 publications

An Observational Investigation of Reverse Engineers' Process and Mental Models

An Observational Investigation of Reverse Engineers' Process and Mental Models

Clustrophile 2: Guided Visual Clustering Analysis

Visual Task Characterization for Exploratory Search

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