Tracking counterfeiting on the web with python and ml
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With the rise of online marketplaces, sellers can easily grow their business by offering their products to a larger number of people. However, this comes with the risk of endangering their reputation, brand trust and profits due to counterfeiting issues. This talk will present a highly scalable approach able to timely query marketplaces via Web scrapers, analyze and classify the obtained data using ML algorithms and summarize the results through visualizations. Join us during this Journey to learn how to identify counterfeit products and discover the underlying technical challenges.
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Graal The Quest for Source Code Knowledge
Source code analysis tools are designed to analyze code artifacts with different intents, which span from improving the quality and security of the software to easing refactoring and reverse engineering activities. However, most tools do not come with features to periodically schedule their analysis or to be executed on a battery of repositories, and lack support to combine their results with other analysis tools. Thus, researchers and practitioners are often forced to develop ad-hoc scripts to meet their needs. This comes at the risk of obtaining wrong results (because of the lack of testing) and of hindering replication by other research teams. In addition, the resulting scripts are often not meant to be customized nor designed for incrementality, scalability and extensibility. In this paper we present Graal, which empowers users with a customizable, scalable and incremental approach to conduct source code analysis and enables relating the obtained results with other software project data. Graal leverages on and extends the functionalities of GrimoireLab, a strong free software tool developed by Bitergia, a company devoted to offer commercial software development analytics, and part of the CHAOSS project of the Linux Foundation.
Measuring Software development with GrimoireLab
With more and more digitized companies and software being a crucial asset to gain competitive advantage, some firms choose to beat competition adopting Open Source to boost innovation. However, this journey requires a clear Software Data Strategy to succeed.
This data-driven approach creates new ways to gather and visualize data to better find meaningful insights among software development activity, performance and community, for future reporting and decision making.
GrimoireLab (https://chaoss.github.io/grimoirelab) produces integrated, open source software for analyzing software and visualizing metrics for measuring community, activity, contributions, performance and project health. Is one of the founding software projects of CHAOSS (Community Health Analytics Open Source Software) project, from The Linux Foundation (https://chaoss.community).
Join us during this Journey from data extraction, gathering, visualization and finally,data interpretation to learn how to measure software development within your Open Source projects with GrimoireLab.
Crossminer and GrimoireLab
Decision makers often face challenges to understand open source projects, especially as data is scattered across different collaboration platforms. Two tools - GrimoireLab and CROSSMINER - were developed to overcome this challenge but were started with different goals in mind. On the one hand, GrimoireLab focused on understanding software development process within open source projects. On the other hand, CROSSMINER focused on helping software developers select the right open source projects. The meaning of project health is different in both cases. This talk is about this philosophical difference in approaching open source project health metrics, how it shaped the tools, what this means for metrics that were implemented, and what lessons can be learned about measuring open source project health.
Perceval: Software Project Data at Your Will
Perceval is a software tool that gathers data from various sources and formats it consistently. It retrieves information like issues, commits, and other items from sources like GitHub. Perceval can be run from the command line or used as a Python library. It allows users to analyze software project data over time to answer questions about new contributors, bugs fixed, and changes in gender diversity. Perceval retrieves data and stores it in a standardized format to facilitate analysis of software projects and communities.
Extending grimoirelab
How to extend GrimoireLab capabilities. Using Perceval and Panels components to analyse Marvel comics network
Perceval
Perceval is the GrimoireLab component that fetches data from data sources, usually by interacting with service APIs or logs. A part of it is generic for all data sources, but most of its code is made of retrievers, each of them specialized to interact with a specific data source. This presentation shows the structure of a retriever, its functionalities and an example
Gamification pres-scme-2017
Conceptual modeling is an essential activity in the development of any information system. But at the same time, it is a difficult one which may discourage some professionals that may be tempted to skip it altogether in favor of more code-oriented tasks. In recent years, gamification has emerged as a new
approach to increase learner engagement and has been successfully applied to a wide range of training and educational scenarios. We believe gamification can play a key supportive role in teaching and learning conceptual modeling, an area
where gamification has not been really applied so far.
We present a model-based approach for gamifying scenarios
for learning modeling. Our approach includes a new language for modeling the gamification process itself and an environment where this new language can be embedded in current modeling tools to allow instructors and students design and use gaming scenarios all within a full modeling infrastructure.
A Model-Based Approach for Extracting Business Rules out of Legacy Informatio...
The document summarizes Valerio Cosentino's PhD thesis defense on extracting business rules from legacy information systems. It outlines his business rule extraction framework, which identifies business terms and rules in source code like COBOL and Java, as well as relational databases. The framework represents extracted rules using text, graphs, and a vocabulary to link rules to their implementations in a traceable way for both technical and business users. It was evaluated on an IBM COBOL application where extracted rules were verified by IBM experts.
Gitana: a SQL-based Git Repository Inspector
Software development projects are notoriously complex and difficult to deal with. Several support tools such as issue tracking, code review and Source Control Management (SCM) systems have been introduced in the past decades to
ease development activities. While such tools efficiently track the evolution of a given aspect of the project (e.g., bug reports), they provide just a partial view of the project and often lack of advanced querying mechanisms limiting themselves to command line or simple GUI support. This is particularly true for projects that rely on Git, the most popular SCM system today.
In this paper, we propose a conceptual schema for Git and an approach that, given a Git repository, exports its data to a relational database in order to (1) promote data integration with other existing SCM tools and (2) enable writing queries on Git data using standard SQL syntax. To ensure efficiency, our approach comes with an incremental propagation mechanism that refreshes the database content with the latest modifications. We have implemented our approach in Gitana, an open-source tool available on GitHub (https://github.com/SOM-Research/Gitana).
Assessing the Bus Factor of Git Repositories
Software development projects face a lot of risks
(requirements inflation, poor scheduling, technical problems, etc.). Underestimating those risks may put in danger the project success. One of the most critical risks is the employee turnover, that is the risk of key personnel leaving the project. A good indicator to evaluate this risk is to measure the concentration of information in individual developers. This is also popularly known as the bus factor (“number of key developers who would need to be incapacitated, i.e. hit by a bus, to make a project
unable to proceed”).
Despite the simplicity of the concept, calculating the actual
bus factor for specific projects can quickly turn into an errorprone and time-consuming activity as soon as the size of the project and development team increase. In order to help project managers to assess the bus factor of their projects, in this paper we present a tool that, given a Git-based repository, automatically measures the bus factor for any file, directory and branch in the repository and for the project itself. You can also simulate with the tool what would happen to the project (e.g., which files would
become orphans) if one or more developers disappeared.
A Model-Driven Approach to Generate External DSLs from Object-Oriented APIs
Developers in modern general-purpose programming languages create reusable code libraries by encapsulating them in Applications Programming Interfaces (APIs). Domain-specific languages (DSLs) can be developed as an alternative method for code abstraction and distribution, sometimes preferable to APIs because of their expressivity and tailored development environment. However
the cost of implementing a fully functional development environment for a DSL is generally higher. In this paper we propose DSLit, a prototype-tool that, given an existing API, reduces the cost of developing a corresponding DSL by
analyzing the API, automatically generating a semantically equivalent DSL with
its complete development environment, and allowing for user customization. To build this bridge between the API and DSL technical spaces we make use of existing
Model-Driven Engineering (MDE) techniques, further promoting the vision of MDE as a unifying technical space.
Extracting Business Rules from COBOL: A Model-Based Framework
This document presents a model-based framework for extracting business rules from COBOL programs. The framework includes steps for discovering the model from COBOL code, identifying business terms and rules, and representing the rules both textually and visually. An early validation on a sample IBM COBOL application found that the framework could identify complete business rules, though some included technical details. Future work includes further validation and expanding the framework to extract rules across all layers of an application.
Extracting UML/OCL Integrity Constraints and Derived Types from Relational Da...
Relational databases usually enforce relevant organizational business rules. This aspect is ignored by current database reverse engineering approaches which only focus on the extraction of the structural part of the conceptual schema.
Other database elements like triggers, views, column constraints, etc. are not considered by those methods. As a result, the generated conceptual schema is incomplete
since integrity constraints and derivation rules enforced by the database are not represented.
In this sense, this paper extends existing approaches by enriching the generated (UML) conceptual schema with a set of OCL integrity constraints and derivation rules inferred from the database schema. Our method has been implemented in a prototype tool for the Oracle database management system.
A Model Driven Reverse Engineering framework for extracting business rules ou...
The document describes a model-driven framework for extracting business rules from Java applications. The framework uses model discovery to generate a platform-specific model from source code. It then performs variable classification to identify domain variables and business concepts. Business rule identification works by "slicing" the model based on domain variables to annotate statements, methods, and declarations relevant to each variable. The annotations are used to extract a business rule model represented using IBM's business rule metamodel. The overall goal is to modularly discover and represent business rules implemented in an application's source code.
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Tracking counterfeiting on the web with python and ml
- 1. Tracking counterfeiting on the Web with Python and ML Valerio Cosentino Software Engineer PyConEs, October 3rd, 2021
- 4. [1] https://www.cbc.ca/news/business/marketplace-counterfeits-fakes-online-shopping-1.5470639 [2] https://apnews.com/press-release/pr-businesswire/ef15478fa38649b5ba29b434c8e87c94 [3] https://www.cnbc.com/2020/03/02/shop-safe-act-2020-cracks-down-on-counterfeits-on-ecommerce-platforms.html Buyer Marketplace Brand
- 5. Buyer Marketplace Brand [1] https://arstechnica.com/tech-policy/2021/05/amazon-seized-and-destroyed-2-million-counterfeit-products-in-2020/ [2] https://www.ebay.com/help/policies/prohibited-restricted-items/counterfeit-item-policy?id=4276#section1 [3] https://www.aliexpress.com/buyerprotection/how_to_be_eligible.html [4] https://ec.europa.eu/growth/industry/policy/intellectual-property/enforcement/memorandum-understanding-sale-counterfeit-goods-internet_en ? ? ?
- 6. How can a brand know if its products are being counterfeiting on the Web? search extract evaluate get crazy Can Python and ML help?
- 7. How can a brand know if its products are being counterfeiting on the Web? search extract evaluate get crazy Can Python and ML help?
- 8. EXTRACT ANALYSIS etc.. SEARCH REPORT How can a brand know if its products are being counterfeiting on the Web?
- 9. queries marketplace product URLs How to write effective queries? How to set the frequency of queries? SEARCH
- 13. ANALYSIS What is a relevant content? What is a legal/illegal content? Relevance Detection
- 14. ANALYSIS What is a relevant content? What is a legal/illegal content? Relevance Detection manual text analysis image features
- 15. ANALYSIS What is a relevant content? What is a legal/illegal content? Relevance Detection rule-based manual text analysis feature analysis manual text analysis image features
- 16. [1] https://www.amazon.com/report/infringement [2] https://sell.aliexpress.com/zh/__pc/77Y4QdcvjD.htm [3] https://pages.ebay.com/seller-center/listing-and-marketing/verified-rights-owner-program.html [4] https://merchant.wish.com/brand-protection/brand-violation-report Fake product URLs Takedown REPORT
- 17. Takeaways ● Counterfeiting is a growing problem ● Python and Machine Learning can help ● Manual intervention is still needed ● The approach can be applied to other scenarios What’s next? ● More data, more questions to answer ○ Evolutionary analysis ○ Comparative analysis