Stem bad$ (all stems)

68 papers:

HT-2015-GadirajuSFK #behaviour #categorisation #comprehension

Breaking Bad: Understanding Behavior of Crowd Workers in Categorization Microtasks (UG, PS, BF, RK), pp. 33–38.

RecSys-2015-LarrainTPGN #case study #collaboration #social

Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging (SL, CT, DP, EGG, KN), pp. 269–272.

ECOOP-2015-PradelS #empirical #javascript

The Good, the Bad, and the Ugly: An Empirical Study of Implicit Type Conversions in JavaScript (MP, KS), pp. 519–541.

ICSE-v1-2015-LavalleeR #case study #developer #quality #why

Why Good Developers Write Bad Code: An Observational Case Study of the Impacts of Organizational Factors on Software Quality (ML, PNR), pp. 677–687.

ICSE-v1-2015-TufanoPBOPLP #smell #why

When and Why Your Code Starts to Smell Bad (MT, FP, GB, RO, MDP, ADL, DP), pp. 403–414.

ASPLOS-2015-SridharanDBFSSG #fault #memory management

Memory Errors in Modern Systems: The Good, The Bad, and The Ugly (VS, ND, SB, KBF, JS, JS, SG), pp. 297–310.

CAV-2015-GouwRBBH #java

OpenJDK’s Java.utils.Collection.sort() Is Broken: The Good, the Bad and the Worst Case (SdG, JR, FSdB, RB, RH), pp. 273–289.

ISSTA-2015-GongPSS #javascript #named

DLint: dynamically checking bad coding practices in JavaScript (LG, MP, MS, KS), pp. 94–105.

TACAS-2014-Wang0LWL #automaton #specification

Are Timed Automata Bad for a Specification Language? Language Inclusion Checking for Timed Automata (TW, JS, YL, XW, SL), pp. 310–325.

ICSME-2014-PalombaBPOL #case study #developer #smell

Do They Really Smell Bad? A Study on Developers’ Perception of Bad Code Smells (FP, GB, MDP, RO, ADL), pp. 101–110.

ICPR-2014-FuscoEM #data analysis #locality #network

Bad Data Analysis with Sparse Sensors for Leak Localisation in Water Distribution Networks (FF, BE, SM), pp. 3642–3647.

KDIR-2014-SuciuICDP #learning #word

Learning Good Opinions from Just Two Words Is Not Bad (DAS, VVI, ACC, MD, RP), pp. 233–241.

ASE-2013-PalombaBPOLP #detection #smell #source code #using

Detecting bad smells in source code using change history information (FP, GB, MDP, RO, ADL, DP), pp. 268–278.

AMT-2013-TichyKL #detection #model transformation #performance #smell

Detecting Performance Bad Smells for Henshin Model Transformations (MT, CK, GL), pp. 82–91.

ICSE-2013-Samuelson

Are software patents bad? (keynote) (PS), p. 855.

ASPLOS-2013-CheckowayS #api #why

Iago attacks: why the system call API is a bad untrusted RPC interface (SC, HS), pp. 253–264.

DATE-2012-WangW #memory management

Extending the lifetime of NAND flash memory by salvaging bad blocks (CW, WFW), pp. 260–263.

CSEET-2012-SajeevC

Will They Report It? Ethical Attitude of Graduate Software Engineers in Reporting Bad News (ASMS, IC), pp. 42–51.

WCRE-2012-AbebeATAG #fault #predict #question #smell

Can Lexicon Bad Smells Improve Fault Prediction? (SLA, VA, PT, GA, YGG), pp. 235–244.

CSEET-2011-ChenT #programming #quality #smell

Grading code quality of programming assignments based on bad smells (WKC, PYT), p. 559.

ITiCSE-2011-PauHGW #case study #exclamation #experience #programming #student

Female students’ experiences of programming: it’s not all bad! (RP, WH, MG, JW), pp. 323–327.

ICSM-2011-OssherSL #java #open source

File cloning in open source Java projects: The good, the bad, and the ugly (JO, HS, CVL), pp. 283–292.

SCAM-2011-AbebeHTM #concept #smell #source code

The Effect of Lexicon Bad Smells on Concept Location in Source Code (SLA, SH, PT, AM), pp. 125–134.

ICSE-2011-OlivetoGBPL #identification #smell

Identifying method friendships to remove the feature envy bad smell (RO, MG, GB, DP, ADL), pp. 820–823.

ITiCSE-2010-Williams-KingAC #named

Enbug: when debuggers go bad (DWK, JA, DMNdC), pp. 28–32.

ICPR-2010-WuBT #image #predict

The Good, the Bad, and the Ugly: Predicting Aesthetic Image Labels (YW, CB, CT), pp. 1586–1589.

SIGIR-2010-BuscherDC #quality #random #web

The good, the bad, and the random: an eye-tracking study of ad quality in web search (GB, STD, EC), pp. 42–49.

ECMFA-2010-Corcoran #case study #development #experience #modelling #scalability

The Good, the Bad and the Ugly: Experiences with Model Driven Development in Large Scale Projects at Ericsson (DC), p. 2.

QoSA-2009-GarciaPEM #architecture #smell #towards

Toward a Catalogue of Architectural Bad Smells (JG, DP, GE, NM), pp. 146–162.

VLDB-2009-MoerkotteNS #bound #estimation #fault

Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors (GM, TN, GS), pp. 982–993.

CSMR-2009-GarciaPEM #architecture #identification #smell

Identifying Architectural Bad Smells (JG, DP, GE, NM), pp. 255–258.

WCRE-1999-AbebeHTM99a #smell

Lexicon Bad Smells in Software (SLA, SH, PT, AM), pp. 95–99.

HCI-NT-2009-LeeLS #design

Brain Response to Good and Bad Design (HL, JL, SS), pp. 111–120.

ESEC-FSE-2009-LiuYNMS #order #refactoring #smell

Facilitating software refactoring with appropriate resolution order of bad smells (HL, LY, ZN, ZM, WS), pp. 265–268.

CSMR-2008-TsantalisCC #identification #named #smell

JDeodorant: Identification and Removal of Type-Checking Bad Smells (NT, TC, AC), pp. 329–331.

DLT-2008-FreydenbergerR #problem

Bad News on Decision Problems for Patterns (DDF, DR), pp. 327–338.

ECIR-2008-Mizzaro #evaluation #information retrieval #question

The Good, the Bad, the Difficult, and the Easy: Something Wrong with Information Retrieval Evaluation? (SM), pp. 642–646.

SIGIR-2008-Al-MaskariSCA #effectiveness #predict #question

The good and the bad system: does the test collection predict users’ effectiveness? (AAM, MS, PDC, EA), pp. 59–66.

DocEng-2007-LuT #documentation #image

Thresholding of badly illuminated document images through photometric correction (SL, CLT), pp. 3–8.

ICDAR-2007-LuT07a #documentation #estimation #image

Binarization of Badly Illuminated Document Images through Shading Estimation and Compensation (SJL, CLT), pp. 312–316.

ICSM-2007-FokaefsTC #identification #named #smell

JDeodorant: Identification and Removal of Feature Envy Bad Smells (MF, NT, AC), pp. 519–520.

SEKE-2007-PivetaHMPAGP #aspect-oriented #smell

Avoiding Bad Smells in Aspect-Oriented Software (EKP, MH, AMDM, MSP, JA, PG, RTP), p. 81–?.

OOPSLA-2007-BondNKGM #fault #null

Tracking bad apples: reporting the origin of null and undefined value errors (MDB, NN, SWK, SZG, KSM), pp. 405–422.

PADL-2007-Bond #declarative #modelling

BAD, a Declarative Logic-Based Language for Brain Modeling (AHB), pp. 200–214.

ICSE-2007-MartinRRS #testing

“Good” Organisational Reasons for “Bad” Software Testing: An Ethnographic Study of Testing in a Small Software Company (DM, JR, MR, IS), pp. 602–611.

SOSP-2007-CostaCZZP #named

Bouncer: securing software by blocking bad input (MC, MC, LZ, LZ, MP), pp. 117–130.

SOSP-2007-TanYKZ #debugging

/*icomment: bugs or bad comments?*/ (LT, DY, GK, YZ), pp. 145–158.

CSL-2007-Murawski

Bad Variables Under Control (ASM), pp. 558–572.

DAC-2006-Josephson #debugging

The good, the bad, and the ugly of silicon debug (DJ), pp. 3–6.

SIGMOD-2006-LometVB #transaction

Recovery from “bad” user transactions (DBL, ZV, RSB), pp. 337–346.

FASE-2006-WestphalT #sequence chart

The Good, the Bad and the Ugly: Well-Formedness of Live Sequence Charts (BW, TT), pp. 230–246.

KR-2006-BoothMW #how

A Bad Day Surfing Is Better than a Good Day Working: How to Revise a Total Preorder (RB, TAM, KSW), pp. 230–238.

ICSM-2004-MantylaVL #smell

Bad Smells — Humans as Code Critics (MM, JV, CL), pp. 399–408.

ICSM-2003-MantylaVL #empirical #smell #taxonomy

A Taxonomy and an Initial Empirical Study of Bad Smells in Code (MM, JV, CL), pp. 381–384.

OOPSLA-2003-ClarkeRN

Saving the world from bad beans: deployment-time confinement checking (DGC, MR, JN), pp. 374–387.

ICSE-2002-ElssamadisyS #programming #smell

Recognizing and responding to “bad smells” in extreme programming (AE, GS), pp. 617–622.

KDD-2001-Kohavi #e-commerce #mining

Mining e-commerce data: the good, the bad, and the ugly (RK), pp. 8–13.

RE-2001-Kovitz #backtracking #development #learning

Is Backtracking so Bad? The Role of Learning in Software Development (BK), p. 272.

SIGMOD-2000-Gal #data transformation

Data Management in eCommerce: The Good, the Bad, and the Ugly (AG), p. 578.

VLDB-2000-RamasamyPNK #set

Set Containment Joins: The Good, The Bad and The Ugly (KR, JMP, JFN, RK), pp. 351–362.

TOOLS-USA-2000-Dedene #cobol #object-oriented

Object-Oriented COBOL, The Old, The Bad and The Ugly [Abstract] (GD), pp. 489–490.

TOOLS-USA-2000-Lauinger00a

Good Software under Bad Conditions (TL), pp. 433–434.

WICSA-1999-LassingRV #architecture #flexibility

Flexibility of the ComBAD Architectures (NHL, DBBR, HvV), pp. 341–356.

CSCW-1996-HeathL #documentation

Documents and Professional Practice: “Bad” Organisational Reasons for “Good” Clinical Records (CH, PL), pp. 354–363.

INTERCHI-1993-Farrand #user interface #visual notation

Common elements in today’s graphical user interfaces: the good, the bad, and the ugly (ABF), pp. 470–473.

KBSE-1991-FeatherFH #design

Composite System Design: The Good News and the Bad News (MSF, SF, BRH), pp. 16–25.

CHI-1990-GentnerG #interface #why

Why good engineers (sometimes) create bad interfaces (DRG, JG), pp. 277–282.

OOPSLA-1989-HarrisonSS #development #experience #object-oriented #paradigm #using

Good News, Bad News: Experience Building a Software Development Environment Using the Object-Oriented Paradigm (WHH, JJS, PFS), pp. 85–94.