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Defect Prediction Over Software Life Cycle in Automotive Domain

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Defect Prediction Over Software Life Cycle in Automotive Domain

Presented at:
9th International Joint Conference on Software Technologies (ICSOFT-EA), Vienna, Austria

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Published in: Automotive
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Defect Prediction Over Software Life Cycle in Automotive Domain

  1. 1. Defect Prediction Over Software Life Cycle in Automotive Domain Rakesh Rana1, Miroslaw Staron1, Jörgen Hansson1, Martin Nilsson2 1Computer Science & Engineering, Chalmers | University of Gothenburg, Sweden 2Volvo Car Group, Gothenburg, Sweden rakesh.rana@gu.se
  2. 2. This Car Runs on Code “It takes dozens of microprocessors running 100 million lines of code to get a premium car out of the driveway, and this software is only going to get more complex” - IEEE spectrum Ref: http://spectrum.ieee.org/green-tech/advanced-cars/this-car-runs-on-code
  3. 3. Software Defect Prediction (SDP) methods Image 1: https://www.reliablesoft.net/how-to-become-an-expert-in-your-niche-even-if-you-are-not/ Image 2: Fenton, Norman, et al. "Predicting software defects in varying development lifecycles using Bayesian nets." Information and Software Technology 49.1 (2007): 32-43. Image 3: Kan, Stephen H. Metrics and models in software quality engineering. Addison-Wesley Longman Publishing Co., Inc., 2002. Image 4: http://www.codeodor.com/index.cfm/2009/11/12/Its-Not-Your-Fault-Your-Software-Sucks/3058
  4. 4. Automotive Software: development process & type Image Source: Rana, Rakesh, et al. "Predicting Pre-release Defects and Identifying Risky Modules Using Prior Iterations Defect Count." Submitted to Software Quality Journal.
  5. 5. Automotive Software: Life Cycle
  6. 6. Support organizations in automotive domain with: – Methods for Software Defect Predictions (SDP) – When in SW life cycle different SDP methods are applicable? – What granularity and for what purpose different SDP techniques can be used in automotive software domain? Objectives
  7. 7. Which prediction model & when? (Automotive SW lifecycle)
  8. 8. Objectives Method Input Data Required Advantages and Limitations Causal Models Inputs about estimated size, complexity, qualitative inputs on planned testing and quality requirements. • Causal models biggest advantage is that they can be applied very early in the development process. • Possible to analyse what-if scenarios to estimate output quality or level of testing needed to meet desired quality goals. Expert Opinions Domain experience (software development, testing and quality assessment). • This is the quickest and most easy way to get the predictions (if experts are available). • Uncertainty of predictions is high and forecasts may be subjected to individual biases. Analogy Based Predictions Project characteristics and observations from large number of historical projects. • Quick and easy to use, the current project is compared to previous project with most similar characteristics. • Evolution of software process, development tool chain may lead to inapplicability or large prediction errors. … … …
  9. 9. The granularity: • Product Level (PL), • System Level (SL), • Sub-System level (SSL), • Functional Unit level (FU), • MOdule (MO), or at the • File Level (FL) Model Application level Application area Causal Models PL, SL, SSL RPA, WIF Expert Opinions PL, SL, SSL, FU RPA, RRA, RCA, WIF Analogy Based Predictions PL, SL, SSL, FU RPA, RRA COQUALMO PL, SL, SSL, FU RPA Correlation Analysis SSL, FU, MO, FL RRA, IDP, WIF Regression Models SSL, FU, MO, FL RRA, IDP, WIF ML based models SSL, FU, MO, FL RRA, IDP, WIF SRGMs PL, SL RPA, RR, RCA At what granularity & for what purpose? Useful applications: • Resource Planning and Allocations (RPA), • What-IF analysis (WIF), • Release Readiness Assessment (RR), • Root Cause Analysis (RCA), or for • Identification of Defect Prone units (IDP)
  10. 10. – Methods for Software Defect Predictions (SDP) – When in SW life cycle different SDP methods are applicable? – What granularity and for what purpose different SDP techniques can be used in automotive software domain? Conclusions

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