ソフトウェア品質シンポジウム2016

Automotive Software Engineering Basics & Robotics & Outlook to Software fault prediction ソフトウェア品質シンポジウム2016

こんにちは

よろしくお願いします 3 ソフトウェア品質シンポジウム2016 AUDI? What are we known for?

Source: AUDI AG 4 ソフトウェア品質シンポジウム2016 My roots

Name Harald Altinger Education ► MSc. Graz, University of Technology ► Robotics & Automotive Engineering Department ► Pre-Development Advanced Robotics ► RoboCup Middle Size League Driver Assistance Systems ► Piloted Parking @ Audi ► I/AEV-31

Today

Ingolstadt, Germany

Austria, Salzburg 5 ソフトウェア品質シンポジウム2016 Topic

› Automotive Basics › Short introduction and overview to automotive development processes › Software Testing › Used tools to develop & test software › Recall & Costs › Robotics & Cars  piloted Driving › Basics in Robotic, including in use sensor for self driving cars › Basic architecture from Audis piloted driving for parking garages › Audis Piloted Driving › Motivation › Architecture › Piloted Driving Demonstrations › Automotive Software Analysis › Industrial Software Metric Dataset › Software Fault prediction ソフトウェア品質シンポジウム2016

Basics

Automotive Software 7 ソフトウェア品質シンポジウム2016 Product Live Cycle Automotive Domain vs. Consumer Electronics

► Production ► 4 years ► 4 years (facelift) ► Usage: ► On average ► Development 9 years (GER) ► 3 – years

► iPhone ► Dev. ~ 1 year ► Production. ~ 1 year Source: AUDI AG ► Usage ~ 2 years See [21] 8 ソフトウェア品質シンポジウム2016 Software

Dimensions Source: AUDI AG

Source: AUDI AG

Space Shuttle: 500.000 LOC Aircraft: ~ 1.700.000 - 3.700.000 Premium car: LOC ~10.000.000 - 20.000.000 LOC See [9],[17] 9 ソフトウェア品質シンポジウム2016 Automotive Domain Development of Software

› Functionality › Safety critical functionality › None Safety critical › Hard- & Firm Real-time › Embedded & Limited CPU power

› Misra Guidelines [8] › No pointer › Integer, Float, Structs › No dynamic String types › Static memory

› V-Process based development › Strict time schedules during development › SOP 10 ソフトウェア品質シンポジウム2016 Automotive Domain W-Development Process

› Enhanced V model › Integration of Testing and Test specification

See [16] 11 ソフトウェア品質シンポジウム2016 Development Stages Car Development

Research Pre-Dev Series Dev [20]

• Feasibility • Concepts • Requirements • Prototypes • Series near • Production Hardware optimization • Functional • Car testing / prototypes prototype cars

SD ... Series Development ADAS … Advanced Driver Assistance System PD … Pre(Series) Development ACC … Adaptive Cruise Control RE … Research CAN … Controller Area Network ESC … Electronic Stability Control ECU … Electronic Control Unit FlexRay … Communication Network ソフトウェア品質シンポジウム2016

Software Testing

Software Tools Methods 13 ソフトウェア品質シンポジウム2016 Development Languages Car Development

See [15] 14 ソフトウェア品質シンポジウム2016 Specification and Testing Tools During Software Development

Specification Tools Test automation Tools

Name of Tool Vendor [%] Name of Tool Vendor [%] Doors IBM 37,62% Exact AEV 10,14% Word Microsoft 16,83% Polyspace MathWorks 10,14% Integrity PTC 12,87% EXAM MicoNova 8,70% Excel Microsoft 10,89% self developed 8,70% Enterprise TPT PikeTec 7,25% Architect SparxSystems 6,93% QA-C QA Systems 5,80% Vector CANoe Vector Informatik 5,80% Jenkins OpenSource 2,90%

See [15] 15 ソフトウェア品質シンポジウム2016 Testing Stages During Car Development

Source: AUDI AG 16 ソフトウェア品質シンポジウム2016 Testing Stages During Software Development

› In The Loop Tests › MiL Model in the Loop › SiL Software in the Loop › PiL Processor in the Loop › HiL Hardware in the Loop › Whole Car › Lab Car › Test Car › Protoype › Pre Production car

Source: AUDI AG 17 ソフトウェア品質シンポジウム2016 Traditional Testing 10 - 15 years ago 18 ソフトウェア品質シンポジウム2016 Software Testing Famous Fails – An excerpt

Ariane 5: 269 Mill EUR USS Yorktown: 3h without North America: Blackout Propulsion

See [7] 19 ソフトウェア品質シンポジウム2016 Motivation Bug statistics

Software Typ Bug rate Costs Industrial Standard 15 – 50 Bugs / 1000 LOC

Microsoft Application 10 – 20 Bugs / 1000 LOC

Clean room development 3 Bugs / 1000 LOC Space Shuttle 0 Bugs / 1000 LOC 1.000 $ / LOC

See [9] 20 ソフトウェア品質シンポジウム2016 Recall Statistic

See [15] 21 ソフトウェア品質シンポジウム2016 Recall Example 22 ソフトウェア品質シンポジウム2016 Recall Costs

› [11]: bug @ dev: 25 $ (85% of all bugs found) bug @ after sales: 16,000 $ › Latest Toyota recall [18] affecting 750.000 vehicles › Workshop costs (per hour): 88 EUR – 222 EUR [10]

Technical Inform Dealership defect Customers

• OEM detect • Get • Perform • Official Addresses maintenance Database • Contact via • Replace Parts (e.g. NHTSA) Mail 23 ソフトウェア品質シンポジウム2016 Recall Costs

› [11]: bug @ dev: 25 $ (85% of all bugs found) bug @ after sales: 16,000 $ › Latest Toyota recall [18] affecting 750.000 vehicles › Workshop costs (per hour): 88 EUR – 222 EUR [10]

Technical Inform Dealership defect Customers

• OEM detect • Get • Perform • Official Addresses maintenance Database • Contact via • Replace Parts (e.g. NHTSA) Mail See [2] 24 ソフトウェア品質シンポジウム2016 Tool Usage

See [15] 25 ソフトウェア品質シンポジウム2016 Testing Methods

See [15] 26 ソフトウェア品質シンポジウム2016 Conclusion

› Main difference between Re and SD. › Re: › Focus on whole vehicle › Specifier, developer & tester within one person › SD: › Focuse on one Unit (ECU) or function › Developer and Tester are dedicated persons › Automated test execution is heavily in use, but no automated test case generation › Formal methods are currently used during specification phase but not for testing › Software-related recalls › Upward trend › May less SW failure per kLOC, but more kLOC per car

Source: AUDI AG ソフトウェア品質シンポジウム2016

Robotics

History Sensors Processors 28 ソフトウェア品質シンポジウム2016 Research Highly Automated Vehicles

Highly automated parking within sub-teran parking garages Boss – DARPA Urban Challenge VaMoRs & VaMP highly automated on the Autobahn Stanley – DARPA Junior – Autonomous Driving Grand Challenge in a Multi-level Parking Structure

Navlab11 – partial automated Valet Parken

1980 2003 2005 2007 2009 2010

Source [3], [5], [6], [12], [13] 29 ソフトウェア品質シンポジウム2016 Audis way to series production zFAS

Source: AUDI AG 30 ソフトウェア品質シンポジウム2016 Basics Sensors Long Range Radar Laser-scanner Video

Top View

Ultrasonic

Mid Range Radar

Source: AUDI AG 31 ソフトウェア品質シンポジウム2016 Basics Central Unit - zFAS

Sensors ACC

Sensors AALA

Sensors PLA

Sensors Topview

Source: AUDI AG ソフトウェア品質シンポジウム2016

Piloted Driving

Motivation Examples Architecture Definition 33 ソフトウェア品質シンポジウム2016 Piloted Driving Audis Approach

Source: AUDI AG 34 ソフトウェア品質シンポジウム2016 Piloted Driving Driver‘s decision upon driving mode

When I don‘t want to drive, I allow myself to be driven!

Traffic Jams

Parking Lots

Source: AUDI AG 35 ソフトウェア品質シンポジウム2016 Piloted Driving Driver‘s decision upon drive mode

If I want to have fun I drive myself!

Source: AUDI AG 36 ソフトウェア品質シンポジウム2016 Piloted Driving Traffic Jam Pilot

► Highly automated driving within traffic jams up to 60 km/h on motorways

► Enhanced comfort due to new usable time within the car ► Enhanced safety due to no exhausting manual drive trough traffic congestion

► New HMI and Infotainment concept to be used during piloted drive mode

Source: AUDI AG 37 ソフトウェア品質シンポジウム2016 Piloted Driving Roadside and Garage Pilot

► Highly automated parking for public parking spaces or private parking garages

► Enhanced comfort due to simpler boarding and deboarding ► Enhanced safety due to avoidance of parking damage

► Smartphone or Car Key can be used to trigger maneuver

Source: AUDI AG 38 ソフトウェア品質シンポジウム2016 Piloted Driving Parking Garage Pilot

► Piloted parking in and out within parking garages

► Enhanced comfort: customer does not need to enter parking garage ► Enhanced safety due to avoidance of parking damage

► Usage of in series sensors in the car ► Usage of reference sensors within the parking garage

Source: AUDI AG 39 ソフトウェア品質シンポジウム2016 Piloted Parking History of Piloted Driving

Source: AUDI AG 40 ソフトウェア品質シンポジウム2016 Piloted Driving Transition from assisted to piloted driving

Piloted Driving Parking Garage Pilot

Traffic Jam Pilot

Traffic Jam Pilot Roadside and Garage Pilot with Lane Change

piloted

Traffic Jam Assistant assisted ACC Stop & Go ACC Parking Assistant Source: AUDI AG 41 ソフトウェア品質シンポジウム2016 Definition of Cars Automation Level Definition See [14] According to SAE

Level 0 Level 1 Level 2 Level 3 Level 4 Level 5 System Driver only Assisted Partly Highly automated Fully Driverless automated automated Lateral Driver Assisted System System System System control (limited time) Longitudinal Driver assisted System System System System control (limited time)

Observer Driver always always Sideline job NO NO NO in car everything everything entertainment Handover never at any time at any time Within defined at domain never time boarder Example ACC Tesla Autopilot Traffic jam Pilot Google Car K.I.T.T.

Source: SAE 43 ソフトウェア品質シンポジウム2016 Piloted Parking A lookout into the feature – a short Clip from 2014 CES

Source: AUDI AG 44 ソフトウェア品質シンポジウム2016 Motivation parking garage pilot Customers

› Parking means no driving pleasure › Consumes time Two-wheelers Cars › Low Speed 45 › Frustrating 40 35 in Millionin › China’s vehicles population 30 25 › Gain 1991-2002: +500%, [1] 20 Vehicles 15 › Searching for a parking spot of 10 › Within cities: Up to 10 minutes, [2] 5 0 Number 1981 1986 1991 1996 2002 Year Development of Chinas vehicle population (see [19]) 45 ソフトウェア品質シンポジウム2016 Parking situations Urban Area

Source: AUDI AG Source: PERFECTPARK 46 ソフトウェア品質シンポジウム2016 Motivation Parking Garage Pilot Parking Garage Operators

› Too low profit for parking services › “Customers won’t pay more for parking only services.”, [4]

› Higher parking density › Better economics › Optimized construction of parking garages

› Dual usage manual & piloted

Source: AUDI AG 47 ソフトウェア品質シンポジウム2016 Wireless Payment Additional Services

› System description › RFID or license plate based vehicle identification › Billing performed by content provider

› Interaction › No interaction required to open barrier › Online personalization by Audi › Information can be displayed in vehicle

› Pilot Region Ingolstadt › Started May 2013

Source: AUDI AG 49 ソフトウェア品質シンポジウム2016 Piloted Parking within a Parking Garage Animated Usecase

Source: AUDI AG 50 ソフトウェア品質シンポジウム2016 Piloted Parking within a Parking Garage Scenario

Hand-Over Zone Piloted Driving Area

Source: AUDI AG 51 ソフトウェア品質シンポジウム2016 System Architecture and Modules Overview

VEHICLE

PARKING GARAGE Source: AUDI AG 52 ソフトウェア品質シンポジウム2016 System Architecture and Modules Overview

Localisation Local Sensors Position fusion ► Ego motion ► Ultrasonic ► Motion model ► Acceleration ► Plausibility check ► Rotation ► Wheel encoders Map ► Static objects ► Dynamic objects ► Infrastructure VEHICLE Feedback controller Pathplaning Decision logoc ► Longitudinal and ► Path segments ► Control logic cross rail guiance Actuators ► Safety logic ► Steering ► Brakes ► Peripheripals ► Power train

Car2x Gateway ► CRC PARKING GARAGE VEHICLE Source: AUDI AG 53 ソフトウェア品質シンポジウム2016 System Architecture and Modules Overview

Car2x Gateway

► CRC

Parkhausmanagement Localisation Parking Garage Sensores

► Control logics ► Position ► LIDAR ► Monitoring hypothesis ► Light beam ► Watchdog ► Cameras

Billing Parking Spots MAP

► Parking duration ► Occupacy ► Dimensions ► Parking rates ► Reservation ► Driveables ► Services ► Parking spots PARKING PARKING GARAGE GARAGE Source: AUDI AG 54 ソフトウェア品質シンポジウム2016 System Architecture and Modules Overview

Customer

VEHICLE

802.11p

802.11 a/b/g/n

PARKING GARAGE Source: AUDI AG 55 ソフトウェア品質シンポジウム2016 System Architecture and Modules Overview

Customer

VEHICLE

802.11p

802.11 a/b/g/n

PARKING GARAGE Source: AUDI AG 56 ソフトウェア品質シンポジウム2016 Results Reproduceable Parking Accuracy

Deviation Min Max Average Standard Dx 43 cm 80,0 cm 5,0 cm 6,1 cm Dx Dy 00 cm 24,4 cm 3,1 cm 5,4 cm Dy Dd 0,00° 5,712° 1,392° 1,291° Dd 20 measurements, traveling path = 70 m

traveling path

Source: AUDI AG ソフトウェア品質シンポジウム2016

Demonstrations

Las Vegas Hockenheim Ingolstadt Berlin 58 ソフトウェア品質シンポジウム2016 Piloted Parking Current Steps

Source: AUDI AG 59 ソフトウェア品質シンポジウム2016 Audi @ CES 2014 Piloted parking with zFAS

Source: Bob Yen Source: Bob Yen 62 ソフトウェア品質シンポジウム2016 Audi Racepilot 240km/h without a driver @ the Race Track

Source: AUDI AG 63 ソフトウェア品質シンポジウム2016 Audi @ Ingolstadt 2014 Piloted parking within public parking Garage

Source: Audi AG 64 ソフトウェア品質シンポジウム2016 Audi @ USA 2015 Piloted Driving from San Francisco to Las Vegas

Source: Auto Express 65 ソフトウェア品質シンポジウム2016 Audi @ Berlinale 2016 Piloted RedCarpet

Source: AUDI AG 66 ソフトウェア品質シンポジウム2016 Audi @ Berlinale 2016 Brandenburger Tor

Source: Audi AG ソフトウェア品質シンポジウム2016

Automotive Software analysis

MSR 2015 SANER 2016 ICTSS 2016 68 AUDI AG I/XX Präsentationstitel Datum ソフトウェア品質シンポジウム2016

Industrial Dataset

MSR 2015 69 ソフトウェア品質シンポジウム2016 Software Lifecycle Development phase

LOC

Requirements Freez

100% Software

time SOP Feature Freez

See [1] 70 ソフトウェア品質シンポジウム2016 Dataset on Automotive Projects Keyfigures

software type function files -Projects

Requirements Testcases src. Files commited prone error Authors Project # # Sub LOC # # # # mdl # files # files Safety AUTOSAR

logic, A 304 13 12.465 185 4 45 26 1782 78 yes no timing dependent behaviour logic, L 600 8 10.113 680 3 20 47 2892 73 yes yes timing dependent behaviour mainly logic operations, K 900 24 36.526 695 5 53 48 2481 329 yes yes branching

See [1] 71 ソフトウェア品質シンポジウム2016 Dataset on Automotive Projects Download

Get the Audi Dataset: http://www.ist.tugraz.at/_attach/Publish/AltingerHarald/MSR_2015_dataset_automotive.zip 72 ソフトウェア品質シンポジウム2016 Automotive Domain MSR 2015 - Dataset

See [1] 73 ソフトウェア品質シンポジウム2016 Dataset on Automotive Projects Marking a Bug – SZZ Algorithm

See [1] 74 AUDI AG I/XX Präsentationstitel Datum ソフトウェア品質シンポジウム2016

Error Class Analysis

SANER 2016 75 ソフトウェア品質シンポジウム2016 Research Questions To be answered

› RQ1 › Is the software metric affected by any of the bugs reported? › RQ 2 › Which type of error classes might be already prevented by existing techniques? › RQ 3 › Do complexity / length of boolean conditions and number of bugs correlate?

Source: AUDI AG 76 ソフトウェア品質シンポジウム2016 Results Operators in use

A K L % Operator % Operator % Operator Logic and comperator 76,38% = 46,17% = 43,88% = 6,60% && 15,14% && 19,78% == 3,59% ! 12,39% ! 10,33% && 2,90% || 9,02% & 6,49% !=

See [23] 77 ソフトウェア品質シンポジウム2016 Results Operators in use

A K L % Operator % Operator % Operator Flow controll 35,31% if 55,49% if 47,46% if 24,91% else 34,42% else 24,73% else 17,55% break 3,40% break 12,27% break 14,12% case 2,62% case 9,04% case

See [23] 78 ソフトウェア品質シンポジウム2016 Results Datatypes in use

A K L % Operator % Operator % Operator Datatypes 95,38% static int 95,90% static int 99,06% static int 4,62% struct 1,91% int 0,94% struct 1,91% unsigned 0,29% struct

See [23] 79 ソフトウェア品質シンポジウム2016 Results Most common Bugs Discovered

explanation

rank type Project LOC McCab N1 N2 n1 n2 Boolean K 1 miscalculations with boolean conditions n n n n n n logic error L missing K 2 missing variable within condition n w y y w w condition L Casting variable type cast error, including K 3 n n n n n n error wronge variable declarations L mistmatch A 4 boolean, e.g. < instead of << n n w w w w K bitwise logic

See [23] 82 ソフトウェア品質シンポジウム2016 Results Boolean length and error distribution

#operands within error prone statements 1 34,92 % 2 06,35 % 3 15,87 % 4 17,46 % 5 01,59 % 6 03,17 % 7 03,17 % 8 07,94 % 9 04,76 % 10 01,59 % 11 03,17 % See [23] 83 ソフトウェア品質シンポジウム2016 Research Questions Review

› RQ1 › Is the software metric affected by any of the bugs reported? See Table III › RQ 2 › Which type of error classes might be already prevented by existing techniques? › RQ 3 › Do complexity / length of boolean conditions and number of bugs correlate? See Table III

Source: AUDI AG 84 AUDI AG I/XX Präsentationstitel Datum ソフトウェア品質シンポジウム2016

Fault Prediction

ICTSS 2015 85 ソフトウェア品質シンポジウム2016 Fault Prediction Industrial Applications

› Low number of Industrial reports › Benefit by strict development guidelines › Ostrand et. al [5] › AT & T Telecom Code › 80% true positives › Logistic Regression › Zimmerman et. al [6] › Microsoft Products (Windows Server 2003, InternetExplorer, etc. ) › Only 3.4% of tested project achieve > 0,75 recall on cross Project prediction › Menzis et. al [7] › Machine Learning (OneR, J48, and Naive Bayes) › 71% true positives › Altinger et. al [24] › Machine Learning (SVM) › 89% true positives 86 ソフトウェア品質シンポジウム2016 Research Questions To be answered

› RQ1 › Can CPFP be applied in projects using automatically generated code and restrictive coding standards like MISRA [26] which was developed for the same target platform? › H1: Similar Code, multiple positive reports on within project prediction in literature › H2: restrictive Setting of Project › RQ 2 › What are the in use fault prediction models reasons to perform low? › H3: strong correlation between metrics › H4: Metrics carry information about faults › H5: Metric areas for faults are similar between projects

Source: AUDI AG 89 ソフトウェア品質シンポジウム2016 PCA › Drawbacks: › No overlap between faulty › Metric regions regions 90 ソフトウェア品質シンポジウム2016 Research Questions review

› RQ1 › Can CPFP be applied in projects using automatically generated code and restrictive coding standards like MISRA [26] which was developed for the same target platform? › H1: Similar Code, multiple positive reports on within project prediction in literature › H2: restrictive Setting of Project › RQ 2 › What are the in use fault prediction models reasons to perform low? › H3: strong correlation between metrics  NO › H4: Metrics carry information about faults  weak correlated › H5: Metric areas for faults are similar between projects  NO

Source: AUDI AG 91 ソフトウェア品質シンポジウム2016 Concluding remarks

› Threads › Only limited scope of automatic code generated software › Strong dependence to code generation templates › Low error number (4 – 6% of all commits contain bugs) › Outlook › Model level metrics › Bug signatures to transfer instead of metric transfer › Thanks › To all three Project Managers and their patience during Interviews

Source: AUDI AG ソフトウェア品質シンポジウム2016

Literatur 93 ソフトウェア品質シンポジウム2016 Piloted Parking within a Parking Garage Literature

[1] H. Altinger, S. Siegl, Y. Dajsuren, and F. Wotawa, “A Novel Industry Grade Dataset for Fault Prediction based on Model-Driven Developed Automotive Embedded Software,” in Proceedings of the 12th Working Conference on Mining Software Repositories, Florence, Italy, 2014. [2] J. Capers, “A short history of the cost per defect metric.” Capers Jones & Associates LLC, 05-May-2009. [3] A. Schanz, A. Spieker, and K. Kuhnert, “Autonomous parking in subterranean garages-a look at the position estimation,” in Intelligent Vehicles Symposium, 2003. Proceedings. IEEE, 2003, pp. 253–258. [4] D. McCandless, P. Doughty-White, and M. Quick, “Codebases - Millions of lines of code.” Information is Beautiful, 26-Nov-2014. [5] P. Waldmann and D. Diehues, “der BMW TrackTrainer - automatisiertes fahren im Grenzbereich auf der Nürnburgring Nordschleife,” 4 Tag. Sicherh. Durch Fahrerassistenz, 2013. [6] H. J. Wünsche, Kognitive Fahrzeuge der UniBWM: Von VaMoRs zu MuCAR-3. 2008. [7] “List of Software Bugs.” Wikipedia. [8] The Motor Industry Software Reliability Asso- and ciation, MISRA-C:2004 - Guidelines for the use of the C language in critical systems, 2nd ed. Warwickshire: MISRA, 2004. [9] D. Dvorak and others, “NASA study on flight software complexity,” NASA Off. Chief Eng., 2009. [10] K. Arbeiterkammer, “REPARATURPREISE VON KFZ-WERKSTÄTTEN 2013.” Arbeiterkammer Wien, May-2013. 94 ソフトウェア品質シンポジウム2016 Piloted Parking within a Parking Garage Literature

[11] W. P. Klocwork, “Software on Wheels,” Klockwork.com, Oct. 2012. [12] various, “Special Issue on the 2007 DARPA Urban Challenge, Part I,” J. Field Robot., vol. 25, no. 8, 2008. [13] S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, K. Lau, C. Oakley, M. Palatucci, V. Pratt, P. Stang, S. Strohband, c Dupont, L.-E. Jendrossek, C. Koelen, c Markey, C. Rummel, J. van Niekerk, E. Jensen, P. Alessandrini, G. Bradski, B. Davies, S. Ettinger, A. Kaehler, A. Nefian, and P. Mahoney, “Stanley: The robot that won the DARPA Grand Challenge,” J. Field Robot., pp. 661–692, 2006. [14] B. W. Smith, “Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems,” no. On-Road Automated Vehicle Standards Committee, Jan. 2014. [15] H. Altinger, F. Wotawa, and M. Schurius, “Testing Methods Used in the Automotive Industry: Results from a Survey,” in Proc.¥ JAMAICA, San Jose, CA, 2014, pp. 1–6. [16] L. Jin-Hua, L. Qiong, and L. Jing, “The w-model for testing software product lines,” in Computer Science and Computational Technology, 2008. ISCSCT’08. International Symposium on, 2008, vol. 1, pp. 690–693. [17] R. N. Charette, “This car runs on code,” IEEE Spectr., vol. 46, no. 3, p. 3, 2009. [18] Toyote Press Release, “Toyota Announces Voluntary Recall of Certain Toyota Prius, RAV4, Tacoma and Lexus RX 350 Vehicles,” 02-Dec-2014. 95 ソフトウェア品質シンポジウム2016 Piloted Parking within a Parking Garage Literature

[19] Y. Z. John Pucher, Yhong-Ren Peng, Neha Mittal and N. Korattyswaroopam, “Urban Transport Trends and Policies in China and India: Impacts of Rapid Economic Growth,” Transp. Rev., pp. 379–410, 2007. [20] H. H. Braess and U. Seiffert, Vieweg Handbuch Kraftfahrzeugtechnik. Springer Fachmedien Wiesbaden, 2013. [21] M. Broy, I. H. Kruger, A. Pretschner, and C. Salzmann, “Engineering Automotive Software,” Proc. IEEE, vol. 95, no. 2, pp. 356–373, 2007. [22] T. Zimmermann, N. Nagappan, H. Gall, E. Giger, and B. Murphy, “Cross-project defect prediction: a large scale experiment on data vs. domain vs. process,” in Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, 2009, pp. 91–100. [23] H. Altinger, Y. Dajsuren, S. Sieg, J. J. Vinju, and F. Wotawa, “On Error-Class Distribution in Automotive Model- Based Software,” in 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, Osaka, Japan, 2016, pp. 688–692. [24] H. Altinger, S. Herbold, J. Grabowski, and F. Wotawa, “Novel Insights on Cross Project Fault Prediction Applied to Automotive Software,” in Testing Software and Systems, vol. 9447, K. El-Fakih, G. Barlas, and N. Yevtushenko, Eds. Springer International Publishing, 2015, pp. 141–157. [25] B.W. Smith, “SAE Levels of Driving Automation,” in Stanford Cyber Law. 2013, http://cyberlaw.stanford.edu/blog/2013/12/sae-levels-driving-automation. ソフトウェア品質シンポジウム2016

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