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Measuring Agile Software Development
- 1. MEASURING AGILE SOFTWAREDEVELOPMENT MIROSLAW STARON, PROFESSOR IN SOFTWARE ENGINEERING, UNIVERSITY OF GOTHENBURG WILHELM MEDING, SENIOR MEASUREMENT PROGRAM LEADER, ERICSSON
- 2. About us Miroslaw Staron •Professor in Software Engineering at Chalmers | University of Gothenburg – Specialization in software measurement Autonomous artificial intelligence based measurement Measurement knowledge discovery Simulation of outcome before decision formulation Metrological foundations of measurement reference etalons – Over 200 publications – Two books Wilhelm Meding • Senior measurement program leader at Ericsson – Leader of a metrics team and an analytics team – 20% metrics research – Ca. 50 papers published – One book
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- 4. Software Center –a collaboration between 12 companies and 5 universities • We work together to accelerate the adoption of novel approaches to software engineering • Our mission with the Software Center is to contribute to maintaining – and strengthen – Sweden’s leading position in engineering industrial software-intensive products.
- 5. Measurement systems –examples
- 6. Our research onsoftware measurement • Artificial Intelligence and Machine Learning measurement programs • Using machine learning to find new indicators in existing data sets • Using deep learning to create early warnings of product performance degradation • Using deep learning to identify violations of coding guidelines
- 7. MEASURES USED BEFOREAGILE TRANSFORMATION
- 8. Main areas StatusComment Overall Planning We keep the time plan so far. Test status may cause delays. Requirements X1 out of Y1 requirements have been reviewed X2 out of Y2 requirements are linked to test cases X3 out of Y3 requirements have test cases in ”passed” Configuration Management Work ongoing to give new features version numbers. Defect status Defect backlog not decreasing Test progress Function testing: according to plans System testing: behind schedule Network testing: lack of resources Costs Within budget Project “X” status report: April
- 9. Monitoring TR Backlogand test progress
- 10. Defect inflow predictions 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 month-6 month-5 month-4 month-3 month-2 month-1 Design-ready month1 month2 month3 month4 Percentageofdefects(scaledtothepeak) Release:baseline-2 Release: baseline-1 Release: baseline Rayleigh model 1 week 0,00 20,00 40,00 60,00 80,00 100,00 120,00 140,00 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Prediction LCI-mean UCI-mean LCI-individual UCI-individual Actual
- 11. MEASURES DURING AGILETRANSFORMATION ”Embrace flow” ”Optimize for speed” ”Empower teams” ”Nurse main branch” ”Unleash releases”
- 12. Bottlenecks aka Thingsin Queue
- 13. Finding legacy practices– Project’s DNA Agile tranformation Project’s DNA (commits, defects) before Agile Project’s DNA (commits, defects) After Agile Similarity: 20% of activities after the transformation are the same as before
- 14. Velocity • Definition ofvelocity (from Agile) – Velocity: work completed in a given time period – Measure: # story points per sprint
- 15. Stable teams • Definition –How many times did any individual in the organization change team within the measurement period? – Average per organization
- 16. Standard SAFe measures(examples) • Program velocity • Predicability • Number of features planned • Number of features accepted • Number of enabler features planned • Number of enabler features accepted • Number of non-functional tests • Number of stories planned • Number of stories accepted • Unit test coverage • Number of defects • Number of total tests • Percent of automated test • Velocity planned • Velocity actual • Number of refactors
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- 18. AGILE MEASURES INDIVIDUALS ANDINTERACTIONS OVER PROCESSES AND TOOLS WORKING SOFTWARE OVER COMPREHENSIVE DOCUMENTATION CUSTOMER COLLABORATION OVER CONTRACT NEGOTIATION RESPONDING TO CHANGE OVER FOLLOWING A PLAN
- 19. Progress of softwaredevelopment teams, cont. Picture taken from book: “Software Development Measurement Programs. Development, management and evolution” Publisher: Springer ISBN: 978‐3‐319‐91835‐8 X
- 20. Release readiness, example1 Picture taken from book: “Software Development Measurement Programs. Development, management and evolution” Publisher: Springer ISBN: 978‐3‐319‐91835‐8 No of defects Defect removal rate – (Test execution rate – Test pass rate) Release readiness = Indicator forecasts when the product is ready for release given the current development speed
- 21. Release readiness, example2 Picture taken from book: “Software Development Measurement Programs. Development, management and evolution” Publisher: Springer ISBN: 978‐3‐319‐91835‐8 456 4,072 4,072 4,072 0
- 22. Integration related measures MeasureMeasurement Function Stakeholder Information Need Integration effectiveness (number of builds successfully integrated to the main branch) over (number of builds delivered to the main branch) * 100 (in %) per week Integration leader - What is the quality of the builds delivered to the main branch? - What is the quality of the performance of the building tools? Integration waste average time a build has to wait before it can be integrated to the main branch (in minutes) Integration leader What is the waste in the integration process? Integration speed average time it takes for a build to be integrated to the main branch (in minutes) Integration leader How efficient is the building process? Meding W (2017): Sustainable measurement programs for software development companies ‐ What to measure. IWSM Mensura, Gothenburg, Sweden Integration effectiveness Integration waste & Integration speed
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- 24. Architectural dependencies — Architecture weight —Architecture preservation factor — Degree of impact of change — Coupling — Cohesion — Number of components — Number of connectors — Number of symbols How good is our architecture? How maintainable is our architecture? How ”big” is our architecture? Staron M, Meding W (2016): A portfolio of internal quality metrics for software architects (SWQD2017)
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- 26. Speed over velocity RequirementsCoding Code review Code integration Testing Deployment • Speed: time from start of review to end of review (+2 in Gerrit) • Size: numbers of files in a batch • Complexity: • Number of reviewers • Number of reviews • Speed: time from start of testing to ready‐ for‐deployment • Size • Number of files • Number of test cases • Complexity • McCabe • # of assertions • Speed: time from commit until build is ready for testing • Compile speed • UT speed • FT speed • Size: numbers of files in a batch
- 27. AGILE MEASURES INREALITY
- 28. Theory vs Companies’need (excerpt from our study) Measure Theory Company A Company B Velocity ++ ‐‐ ‐‐ Speed ‐‐ ++ ++ Number of releases per year ++ ‐‐ ‐‐ Release readiness ‐‐ ++ ‐‐ Team velocity vs. Capacity ++ ‐‐ ‐‐ Scope creep ‐‐ ‐‐ ++ Burn‐up ‐‐ ++ ++ Number of *‐tests ++ ++ ++ Number of defects ‐‐ ++ ++ Tool status (Up‐time, ISP) ‐‐ ++ ++ Integration status (commits/broken builds) ‐‐ ++ ++
- 29. Depth of using measures (degreesof acceptance) Breadth of using measures (types of measures) Behavior (knowledge of) Performance (to which degree the behavior is performed) Preference (like or dislike) Normative consensus (appropriateness) Value (good or bad behavior) Current status Potential Used Good practice Low hanging fruits
- 30. Beyond Agile Measures AutonomousAI based measurement systems
- 31. • Autonomous AIbased measurement systems • AI based measures discovery • Automated minig of sw measures • Low-code/no-code sw development programs • In-tools sw measurements
- 32. Autonomous AI-based measurement Learningcode quality from Gerrit • Problem – How can we detect violations of coding styles in a dynamic way? Dynamic == the rules can change over time based on the team’s programming style • Solution at a glance – Teach the code counter to recognize coding standards by analyzing code reviews – Use machine learning as the tool’s engine to define the formal rules – Apply the tool on the code base to find violations • Results – 75% accuracy Violations Gerrit reviews Product code base Deep learning Work done together with M. Ochodek and R. Hebig Machine assessment
- 33. Feature acquisition 33 File type#Characters If … Decision class java 25 TRUE … Violation … … … … … Feature engineering and extraction engine Source code: training set Source code: ML encoded training set Data set expansion: Ca. 1,000 LOC ‐> 180,000 LOC
- 34. Input layer …………………………………….… Recurrent layer …………………………………….… Convolution layer ………………………….… Recognize low level patterns (e.g. non‐standard ”for”) Output layer Recognize high level patterns (e.g. non‐compiled code) 90% probability of violation 9.9% probability of non‐violation 0.1% probability of undecided Encoded lines Using deep learningto find patterns 180,000 lines of Gerrit reviews
- 35. Results Recurrent NN Layer (type) Output Shape Param # ================================================================= input (InputLayer) (None, 6000) 0 _________________________________________________________________ embedding_1 (Embedding) (None, 6000, 50) 7650 _________________________________________________________________ conv1d_1 (Conv1D) (None, 6000, 32) 4832 _________________________________________________________________ max_pooling1d_1 (MaxPooling1 (None, 3000, 32) 0 _________________________________________________________________ conv1d_2 (Conv1D) (None, 3000, 32) 3104 _________________________________________________________________ max_pooling1d_2 (MaxPooling1 (None, 1500, 32) 0 _________________________________________________________________ conv1d_3 (Conv1D) (None, 1500, 32) 3104 _________________________________________________________________ max_pooling1d_3 (MaxPooling1 (None, 750, 32) 0 _________________________________________________________________ conv1d_4 (Conv1D) (None, 750, 32) 3104 _________________________________________________________________ max_pooling1d_4 (MaxPooling1 (None, 375, 32) 0 _________________________________________________________________ conv1d_5 (Conv1D) (None, 375, 32) 3104 _________________________________________________________________ dropout_1 (Dropout) (None, 375, 32) 0 _________________________________________________________________ conv1d_6 (Conv1D) (None, 375, 2) 66 _________________________________________________________________ activation_1 (Activation) (None, 375, 2) 0 _________________________________________________________________ global_average_pooling1d_1 ( (None, 2) 0 _________________________________________________________________ loss (Activation) (None, 2) 0 ================================================================= Total params: 24,964 Trainable params: 17,314 Non‐trainable params: 7,650
- 36. Conclusions • What doesagile offer? – Customer focused software development – Faster delivery of new features – Higher quality • How can we get there? – Aligning software measurement with agile software development – Monitor what agile does not explicitly focus on, e.g. stability of architectures – Use modern software measurement technologies and dynamic, actionable dashboards • What does the future holds? – AI and autonomous measurement systems – Assisting developers in software development through self-x measurement systems – Evolving, pro-active measurement systems