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This document discusses interactive debugging and presents an approach called Swarm Debugging. It introduces the Swarm Debug Infrastructure (SDI), an open-source tool that collects and shares fine-grained data about developers' interactive debugging activities. An experimental study was conducted using SDI to analyze debugging patterns and correlations. The study found that there is no correlation between the number of invocations or breakpoints toggled and task time, but developers follow different debugging patterns and toggling breakpoints carefully correlates with faster task completion times. SDI provides an infrastructure for further investigating interactive debugging phenomena.
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Swarm Debug Infrastructure (SDI) is an open-source tool that collects and shares fine-grained data about developers' interactive debugging activities. It allows researchers and practitioners to visualize debugging sessions, create recommendation systems, and perform empirical studies. An experimental study using SDI found that developers typically toggle between one or two breakpoints per debugging task, and exhibit either a "fuzzy" or "straight" navigation pattern through method invocations. SDI aims to preserve and share human debugging knowledge.
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Icpc16.ppt
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Why benchmarking is hard?
A little bit of JVM internals
How JIT optimisations can affect benchmarks
JMH to the rescue
Warmup
Multi-threading
Compiler optimisations
Profiling with JMH
CPU
Memory
Using external profilers
Continous integration
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This document contains the resume of Abhinaya V. She has 1 year of experience as a Software Developer at Indus Teqsite Pvt. Ltd., working on defense and aerospace projects. She has experience with C/C++ development, object-oriented analysis and design, and working in Linux, Xenomai and Windows environments. Some of her projects include developing an ADS-B system for aircraft identification using CPR algorithms in Linux, and developing real-time and non-real-time target software for an imaging seeker system using C, Linux and Xenomai. She has a B.E. in Computer Science and Engineering.
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From IoT to Software Miniaturisation
The document discusses the history and current state of software engineering and its application to IoT systems. It notes that 50 years after the earliest software projects, issues still include cost overruns, property damage, risks to life and death, and challenges ensuring quality. For IoT, fragmentation across hardware, software, APIs and standards poses significant problems. The document proposes that research into IoT software engineering could help address these issues through approaches like developing software to run across diverse IoT platforms, and automatically miniaturizing software through techniques like multi-objective optimization to suit different IoT device capabilities.
Presentation
1) Issue trackers are often used to track more than just bugs, including features, enhancements, and refactoring work.
2) A manual analysis found that nearly half of issues labeled as "bugs" in issue trackers were actually not bugs.
3) Relying on issue tracker labels alone can introduce significant errors into datasets used for tasks like bug prediction and severity estimation. More work is needed to clean noisy and unreliable data.
Presentation
The document discusses how to derive dependency structures for legacy J2EE applications. It proposes analyzing all application tiers together using a language-independent model and parsing various artifacts. Configuration files and limited data flow analysis are used to understand dependencies. Container dependencies are explicitly codified by studying technology specifications and codifying dependency rules to apply when certain code patterns are detected in applications. This allows completing an application's dependency graph.
Presentation
The document discusses the state of practices of service identification in the industry for migrating legacy systems to service-oriented architectures (SOA). It finds that while service identification is seen as important, it remains primarily a manual process focused on identifying coarse-grained business services from source code and business processes. Wrapping and clustering functionalities are common techniques. Fully automating service identification is still challenging due to the need to understand complex legacy system dependencies. The document recommends service identification be business-driven and follow proven methodologies.
Presentation by Lionel Briand
This document discusses techniques for testing advanced driver assistance systems (ADAS) through physics-based simulation. It faces challenges due to the large, complex, and multidimensional test input space as well as the computational expense of simulation. The document proposes using a genetic algorithm guided by decision trees to more efficiently search for critical test cases. Classification trees are built to partition the input space into homogeneous regions in order to better guide the selection and generation of test inputs toward more critical areas.
Manel Abdellatif
The document reports on the findings of a survey of 45 industrial practitioners on their experiences with legacy-to-SOA migrations. The key findings include: 1) Practitioners migrate legacy systems implemented in Cobol and Java to reduce maintenance costs and improve flexibility/interoperability; 2) Identifying services is an important step but is mostly manual and business-driven; 3) The most used techniques are functionality clustering and wrapping; 4) Desired service qualities are reusability, granularity and loose coupling; 5) Identified services prioritize domain-specific over technical services; 6) RESTful services are most targeted technology.
Azadeh Kermansaravi
The document investigates the impact of linguistic anti-patterns (LAs) on program comprehension. It defines LAs as bad naming, documentation, and implementation practices. A study was conducted involving 92 students assessing programs with and without LAs. The study found that LAs negatively impact understandability by increasing time and reducing correctness. Certain LAs like A2, B4, and D1 had a stronger negative effect than others like E1. The study also found that providing knowledge about LAs can help mitigate their impact by making programs easier and faster to comprehend.
Mouna Abidi
The document discusses research on identifying and analyzing the impact of patterns on the quality of multi-language systems. The objectives are to collect and categorize sets of programming languages used together, detect patterns in multi-language programs to track bugs and provide best practices, and study how patterns impact quality. The contributions will be a catalog of multi-language patterns and defects, a detection tool, and analysis of patterns' effects on quality attributes. Current work includes reviewing literature on language combinations and patterns to provide recommendations for high-quality multi-language development.
CSED - Manel Grichi
This document discusses research on change impact analysis in multi-language systems. It begins by outlining recommendations for best practices when using JNI, such as passing primitive types, minimizing calls between native and Java code, and properly handling strings. It then describes a qualitative analysis of JNI usage that identified common practices and issues. Finally, it proposes future work to survey developers on applying recommendations to facilitate change impact analysis in multi-language systems.
Cristiano Politowski
The document summarizes a recommendation system that suggests software processes for video game projects based on similarities to past projects. The system analyzes over 100 postmortems from previous games to build a database of development processes and project contexts. It uses principal component analysis to identify similar past projects and recommends a process by combining elements from similar projects' processes. The system was evaluated both quantitatively based on correctness and coverage metrics and qualitatively through surveys and a case study with a developer team.
Will io t trigger the next software crisis
This document discusses how the rise of the Internet of Things (IoT) could trigger a new software crisis due to issues like fragmentation, complexity, and lack of standards. It provides a brief history of software engineering challenges over the past 50 years such as cost overruns, safety issues, and prioritizing productivity over quality. The document then examines how these same problems are emerging in the IoT context today. It argues that IoT software engineering practices need to address issues like device software, cloud/app development, and privacy in order to avoid a major crisis.
MIPA
This document discusses theories related to software design patterns. It notes that while design patterns are commonly used, there is a need for more research on how they impact software quality. The document proposes several areas for developing theories, including systematically categorizing existing patterns based on underlying principles, combining principles to identify new patterns, and developing theories of patterns from developer behavior and for building software systems. Formalizing patterns and identifying their relationships could help teaching and understanding of patterns.
Thesis+of+laleh+eshkevari.ppt
Laleh M. Eshkevari defended her Ph.D dissertation on developing techniques for the automatic detection and classification of identifier renamings in software projects. Her dissertation outlined a taxonomy of renamings, described approaches for renaming detection based on line mapping, entity mapping and data flow analysis, and discussed methods for classifying renamings based on their form and semantic changes. Evaluation of the approaches on several open source projects showed high precision and recall for renaming detection and identified trends in how renamings are used in practice.
Thesis+of+nesrine+abdelkafi.ppt
Patrons de conception et leur impact sur la modifiabilité des application JEE
Medicine15.ppt
Virtual Intervention to Support Self-management of Antiretroviral Therapy among Persons Living with HIV
Qrs17b.ppt
1) The document analyzes the co-occurrence of code smells like anti-patterns and clones in software systems and their impact on fault-proneness.
2) It finds that over 50% of classes with anti-patterns also have clones, and 59-78% of classes with clones also participate in anti-patterns.
3) Classes with both anti-patterns and clones are significantly more fault-prone than other classes, with the risk of faults being at least 7 times higher in one system studied.
Icpc11c.ppt
Trustrace is an approach that uses software repository links like SVN commits to improve the trust in automatically recovered traceability links between requirements and code. It calculates an initial trust value for links based on IR techniques like VSM, and then reweights the links based on additional information from the software repository. An evaluation on two case studies found Trustrace improved precision over VSM alone and showed no significant difference in recall, supporting the hypothesis that Trustrace can improve link recovery accuracy over IR-only approaches.
Icsme16.ppt
The document presents a taxonomy called ProMeTA for classifying program metamodels used in program reverse engineering. ProMeTA defines characteristics such as target language, abstraction level, meta-language, and more to classify popular metamodels like AST, KDM, FAMIX. The taxonomy aims to provide a comprehensive guide for researchers and practitioners to select, design, and communicate metamodels. The paper also analyzes existing metamodels according to the ProMeTA taxonomy and identifies gaps to guide future metamodel development.
Msr17a.ppt
This document describes a controlled, multiple case study of software evolution and defects from industrial projects. It details the data sources used, including source code repositories, issue tracking databases, and interviews. Metrics such as code smells, size, effort, and defects were collected. Programming skills of developers were also measured. Code smell detection tools and custom scripts to analyze code changes were used to extract metrics on a variety of code issues and evolution over time. The data is available online for further analysis.
Icsoc15.ppt
The document describes a study on detecting linguistic (anti)patterns in RESTful APIs. It presents an approach called DOLAR (Detection Of Linguistic Antipatterns in REST) that analyzes REST API URIs and detects antipatterns using heuristics-based algorithms. Experiments were conducted on 309 methods from 15 public REST APIs to test DOLAR's accuracy, the extensibility of the underlying SOFA framework, and the performance of detection algorithms. The results showed that 42% of methods exhibited contextualized resource names (a pattern) while 14% had contextless resource names (an antipattern), with detection taking under a second on average.
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This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.Essentials of Automations: The Art of Triggers and Actions in FME
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
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Energy consumption of Database Management - Florina Jonuzi
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Graspan: A Big Data System for Big Code Analysis
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
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A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
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Qrs16.ppt
- 1. Towards Understanding Interactive Debugging Fabio Petrillo ∗† , Zéphyrin Soh † , Foutse Khomh † , Marcelo Pimenta∗ , Carla Freitas∗ , Yann-Gaël Guéhéneuc † ∗ Federal University of Rio Grande do Sul, RS, Brazil École Polytechnique de Montréal, QC, Canada IEEE International Conference on Software Quality, Reliability and Security (QRS’2016)
- 3. Is debugging important? Developers spend over two-thirds of their time (68%) investigating code, and the majority of this time is spent debugging code (33%) [LaToza 2010]. LaToza, T. D., & Myers, B. a. (2010). Developers ask reachability questions. 2010 ACM/IEEE 32nd International Conference on Software Engineering, 1, 185–194. 3
- 4. During debugging, a lonely developer navigates the source code steps into many statements traverses dozens of method invocations gains understanding about the system. 4
- 5. if interactive debugging is important to create knowledge about a software project, why is this human knowledge lost? 5
- 6. Can we collect and share debugging session information? 6
- 8. 8
- 9. 9
- 11. Swarm Debug Infrastructure (SDI) is an open- source infrastructure which allows practitioners and researchers to collect and share fine-grained data about developers’ interactive debugging activities. 11
- 13. Architecture 13
- 14. Metadata 14
- 15. 15
- 16. 16
- 17. 17
- 18. 18
- 20. Experiment design ● 5 professional freelancers and 2 students ● Warm-up task ● 5 bug location tasks ● Open source project: JabRef 3.2 ● 19 bug location sessions ● about 6 hours of effective of video recording ● 110 breakpoints and ~ 7000 invocations 20
- 21. Results 3 RQs 21
- 22. RQ1: Is there a correlation between the numbers of invocations or toggled breakpoints and tasks’ elapsed time? 22
- 23. 23
- 24. RQ1: Is there a correlation between the numbers of invocations or toggled breakpoints and tasks’ elapsed time? There is no correlation(ρ = −0.039, 0.093) between the number of invocations(respectively the number of breakpointstoggled) during a debugging session and the spent time on the debugging task 24
- 25. RQ2: Do developers explore/debug in different ways a task? ● we observed two distinct debugging navigation patterns: ● fuzzydebugging pattern (several branches) ● straightdebugging pattern 25
- 26. Fuzzy pattern 26
- 27. 27
- 29. 29
- 30. RQ3: Is there a correlation between time of first breakpoint and task’s elapsed time? 30
- 31. RQ3: Is there a correlation between time of first breakpoint and task’s elapsed time? ● there is a strong correlation (ρ = −0.637) 31
- 32. RQ3: Is there a correlation between time of first breakpoint and task’s elapsed time? ● there is a strong correlation (ρ = −0.637) 32
- 33. 33
- 34. whether developers toggle breakpoints carefully, they complete tasks faster than developers who toggle breakpoints too quickly 34
- 35. ● Swam Debugging is an approach uses Swarm Intelligence and Information Foraging Theory to provide knowledge from interactive debugging session information ● There is a lot to investigate about interactive debugging phenomena! ● Swarm Debug Infrastructure (SDI) provides an infrastructure to create tools for context-aware debugging ● Developers follow different debugging patterns ● When developers toggle breakpoints carefully, they complete tasks faster than developers who toggle breakpoints too quickly ● There is a lot of investigating about interactive debugging phenomena! Final Remarks 35
- 36. ● It is free/open source and open research data ○ https://github.com/SwarmDebugging ○ http://server.swarmdebugging.org/ Swarm Debug Infrastructure is Free! 36
- 37. Do you like work with these data? 37
- 38. Do you like work with these data? Go ahead! 38
- 39. Towards Understanding Interactive Debugging IEEE International Conference on Software Quality, Reliability and Security (QRS’2016) Thank you! @petrillofabio fabio@petrillo.com