A Behavior-Driven Recommendation System for Stack Overflow Posts

Developers are often tasked with maintaining complex systems. Regardless of prior experience, there will inevitably be times in which they must interact with parts of the system with which they are unfamiliar. In such cases, recommendation systems may serve as a valuable tool to assist the developer in implementing a solution. Many recommendation systems in software engineering utilize the Stack Overflow knowledge-base as the basis of forming their recommendations. Traditionally, these systems have relied on the developer to explicitly invoke them, typically in the form of specifying a query. However, there may be cases in which the developer is in need of a recommendation but unaware that their need exists. A new class of recommendation systems deemed Behavior-Driven Recommendation Systems for Software Engineering seeks to address this issue by relying on developer behavior to determine when a recommendation is needed, and once such a determination is made, formulate a search query based on the software engineering task context. This thesis presents one such system, StackInTheFlow, a plug-in integrating into the IntelliJ family of Java IDEs. StackInTheFlow allows the user to intervi act with it as a traditional recommendation system, manually specifying queries and browsing returned Stack Overflow posts. However, it also provides facilities for detecting when the developer is in need of a recommendation, defined when the developer has encountered an error messages or a difficulty detection model based on indicators of developer progress is fired. Once such a determination has been made, a query formulation model constructed based on a periodic data dump of Stack Overflow posts will automatically form a query from the software engineering task context extracted from source code currently open within the IDE. StackInTheFlow also provides mechanisms to personalize, over time, the results displayed to a specific set of Stack Overflow tags based on the results previously selected by the user. The effectiveness of these mechanisms are examined and results based the collection of anonymous user logs and a small scale study are presented. Based on the results of these evaluations, it was found that some of the queries issued by the tool are effective, however there are limitations regarding the extraction of the appropriate context of the software engineering task yet to overcome

Combining configuration and recommendation to enable an interactive guidance of product line configuration

This paper is interested in e-commerce for complex configurable products/systems. E-commerce makes a wide use of recommendation techniques to help customers identify relevant products or services in large collections of offers. One particular way to achieve this is to offer customers a panel of options among which they can select their preferred ones. A trend in the industry is to go a step further, beyond the selection of pre-defined products from a catalogue by handling products customization. The systems engineering community has shown that, based on product line engineering methods, techniques and tools, it is possible to produce customized products efficiently and at low cost. The problem is that there are usually so many products in a PL that it is impossible to specify all of them explicitly, and therefore traditional recommendation techniques cannot be simply applied. This paper proposes an approach that combines two complementary forms of guidance: configuration and recommendation, to help customers define their own products out of a product line specification. The proposed approach, called interactive configuration supports the combination by organizing the configuration process in a series of partial configurations where decisions are made by the recommendation. This paper illustrates this process by applying it to an example with the content based method for recommendation and the a priori configuration approach

State of the art of a multi-agent based recommender system for active software engineering ontology

Software engineering ontology was first developed to provide efficient collaboration and coordination among distributed teams working on related software development projects across the sites. It helped to clarify the software engineering concepts and project information as well as enable knowledge sharing. However, a major challenge of the software engineering ontology users is that they need the competence to access and translate what they are looking for into the concepts and relations described in the ontology; otherwise, they may not be able to obtain required information. In this paper, we propose a conceptual framework of a multi-agent based recommender system to provide active support to access and utilize knowledge and project information in the software engineering ontology. Multi-agent system and semantic-based recommendation approach will be integrated to create collaborative working environment to access and manipulate data from the ontology and perform reasoning as well as generate expert recommendation facilities for dispersed software teams across the sites

Application of Liquid Rank Reputation System for Content Recommendation

An effective content recommendation on social media platforms should be able to benefit both creators to earn fair compensation and consumers to enjoy really relevant, interesting, and personalized content. In this paper, we propose a model to implement the liquid democracy principle for the content recommendation system. It uses a personalized recommendation model based on reputation ranking system to encourage personal interests driven recommendation. Moreover, the personalization factors to an end users' higher-order friends on the social network (initial input Twitter channels in our case study) to improve the accuracy and diversity of recommendation results. This paper analyzes the dataset based on cryptocurrency news on Twitter to find the opinion leader using the liquid rank reputation system. This paper deals with the tier-2 implementation of a liquid rank in a content recommendation model. This model can be also used as an additional layer in the other recommendation systems. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.Comment: Accepted in 2022 Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, Yekaterinburg, Russi