Augmenting Machine Learning with Information Retrieval to Recommend Real Cloned Code Methods for Code Completion

Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones accumulated in these repositories hence represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. In previous work, we introduced DeepClone, a deep neural network model trained by fine tuning GPT-2 model over the BigCloneBench dataset to predict code clone methods. The probabilistic nature of DeepClone output generation can lead to syntax and logic errors that requires manual editing of the output for final reuse. In this paper, we propose a novel approach of applying an information retrieval (IR) technique on top of DeepClone output to recommend real clone methods closely matching the predicted output. We have quantitatively evaluated our strategy, showing that the proposed approach significantly improves the quality of recommendation

SAMOS - A framework for model analytics and management

The increased popularity and adoption of model-* engineering paradigms, such as model-driven and model-based engineering, leads to an increase in the number of models, metamodels, model transformations and other related artifacts. This calls for automated techniques to analyze large collections of those artifacts to manage model-* ecosystems. SAMOS is a framework to address this challenge: it treats model-* artifacts as data, and applies various techniques—ranging from information retrieval to machine learning—to analyze those artifacts in a holistic, scalable and efficient way. Such analyses can help to understand and manage those ecosystems

A systematic review on food recommender systems

The Internet has revolutionised the way information is retrieved, and the increase in the number of users has resulted in a surge in the volume and heterogeneity of available data. Recommender systems have become popular tools to help users retrieve relevant information quickly. Food Recommender Systems (FRS), in particular, have proven useful in overcoming the overload of information present in the food domain. However, the recommendation of food is a complex domain with specific characteristics causing many challenges. Additionally, very few systematic literature reviews have been conducted in the domain on FRS. This paper presents a systematic literature review that summarises the current state-of-the-art in FRS. Our systematic review examines the different methods and algorithms used for recommendation, the data and how it is processed, and evaluation methods. It also presents the advantages and disadvantages of FRS. To achieve this, a total of 67 high-quality studies were selected from a pool of 2,738 studies using strict quality criteria. The review reveals that the domain of food recommendation is very diverse, and most FRS are built using content-based filtering and ML approaches to provide non-personalised recommendations. The review provides valuable information to the research field, helping researchers in the domain to select a strategy to develop FRS. This review can help improve the efficiency of development, thus closing the gap between the development of FRS and other recommender systems.</p

A systematic review on food recommender systems

The Internet has revolutionised the way information is retrieved, and the increase in the number of users has resulted in a surge in the volume and heterogeneity of available data. Recommender systems have become popular tools to help users retrieve relevant information quickly. Food Recommender Systems (FRS), in particular, have proven useful in overcoming the overload of information present in the food domain. However, the recommendation of food is a complex domain with specific characteristics causing many challenges. Additionally, very few systematic literature reviews have been conducted in the domain on FRS. This paper presents a systematic literature review that summarises the current state-of-the-art in FRS. Our systematic review examines the different methods and algorithms used for recommendation, the data and how it is processed, and evaluation methods. It also presents the advantages and disadvantages of FRS. To achieve this, a total of 67 high-quality studies were selected from a pool of 2,738 studies using strict quality criteria. The review reveals that the domain of food recommendation is very diverse, and most FRS are built using content-based filtering and ML approaches to provide non-personalised recommendations. The review provides valuable information to the research field, helping researchers in the domain to select a strategy to develop FRS. This review can help improve the efficiency of development, thus closing the gap between the development of FRS and other recommender systems.</p

A labeled Ecore metamodel dataset for domain clustering

Manually labeled 555 metamodels mined from GitHub in April 2017.  Domains: (1) bibliography, (2) conference management, (3) bug/issue tracker, (4) build systems, (5) document/office products, (6) requirement/use case, (7) database/sql, (8) state machines, (9) petri nets Procedure for constructing the dataset: fully manual, by searching for certain keywords and regexes (e.g. "state" and "transition" for state machines) in the metamodels and inspecting the results for inclusion.  Format for the file names: ABSINDEX_CLUSTER_ITEMINDEX_name_hash.ecore</p

Clone detection for ecore metamodels using N-grams

\u3cp\u3eIncreasing model-driven engineering use leads to an abundance of models and metamodels in academic and industrial practice. A key technique for the management and maintenance of those artefacts is model clone detection, where highly similar (meta-)models and (meta-)model fragments are mined from a possibly large amount of data. In this paper we extend the SAMOS framework (Statistical Analysis of MOdelS) to clone detection on Ecore metamodels, using the framework’s n-gram feature extraction, vector space model and clustering capabilities. We perform a case analysis on Ecore metamodels obtained by applying an exhaustive set of single mutations to assess the precision/sensitivity of our technique with respect to various types of mutations. Using mutation analysis, we also briefly evaluate MACH, a comparable UML clone detection tool.\u3c/p\u3