The extraction of features is an essential step in the process of mining software repositories. An important feature that has been actively studied in the field of mining software repositories is bad code smells. Bad code smells are patterns in the source code that indicate an underlying issue in the design and implementation of the software. Several tools have been proposed to extract code smells. However, currently, there are no tools that extract a significant number of code smells from software written in C++. Therefore, we propose CLEAN++ (Code smeLls ExtrActioN for C++) [1]. It is an extension of a robust static code analysis tool that implements 35 code smells. To evaluate CLEAN++, we ran it over 44 open-source projects and wrote test cases to validate each code smell. Also, we converted the test cases to Java and used two Java tools to validate the effectiveness of our tool. In the end, we confirmed that the CLEAN++ is successful at detecting code smells.The tool is available at https://github.com/Tomma94/CLEAN-Plus-Plus.

Classical model-based diagnosis uses a model of the system to infer diagnoses—explanations—of a given abnormal observation. In this study we address the case where there is uncertainty over a given observation and explore it. This can happen, for example, when the observation outputs are collected by sensors with some noise, that are known to return incorrect value with some probability. We formally define this problem for abductive and consistency-based forms domains. Furthermore, we propose two complete and sound algorithms for finding and ranking all diagnoses and analyze their complexity. We dive deeper and improve the first algorithm's efficiency even more by exploiting past results. Finally, we propose a third algorithm that returns the most likely diagnosis without finding all possible diagnoses, assuming the uncertainty over the observations and the components behavior modes are independents. Experimental evaluation shows that the last algorithm can be very efficient in cases where the diagnosis cardinality, as well as the uncertainty likelihood over the observation, are expected to be small. If, however, all possible diagnoses are desired, then the choice between the first two algorithms depends on whether the domain's diagnosis form is abductive or consistent.

This article provides new techniques for optimizing domain design for goal and plan recognition using plan libraries. We define two new problems: Goal Recognition Design for Plan Libraries (GRD-PL) and Plan Recognition Design (PRD). Solving the GRD-PL helps to infer which goal the agent is trying to achieve, while solving PRD can help to infer how the agent is going to achieve its goal. For each problem, we define a worst-case distinctiveness measure that is an upper bound on the number of observations that are necessary to unambiguously recognize the agent’s goal or plan. This article studies the relationship between these measures, showing that the worst-case distinctiveness of GRD-PL is a lower bound of the worst-case plan distinctiveness of PRD and that they are equal under certain conditions. We provide two complete algorithms for minimizing the worst-case distinctiveness of plan libraries without reducing the agent’s ability to complete its goals: One is a brute-force search over all possible plans and one is a constraint-based search that identifies plans that are most difficult to distinguish in the domain. These algorithms are evaluated in three hierarchical plan recognition settings from the literature. We were able to reduce the worst-case distinctiveness of the domains using our approach, in some cases reaching 100% improvement within a predesignated time window. Our iterative algorithm outperforms the brute-force approach by an order of magnitude in terms of runtime.

A model-based co-clustering divides the data based on two main axes and simultaneously trains a supervised model for each co-cluster using all other input features. For example, in the rating prediction task of recommender system, the main two axes are items and users. In each co-cluster, we train a regression model for predicting the rating based on other features such as user's characteristics (e.g., gender), item's characteristics (e.g., genre), contextual features (e.g., location), and so on. In reality, users and items do not necessarily belong to a single co-cluster, but rather can be associated with several co-clusters. We extend the model-based co-clustering to support fuzzy co-clustering. In this setting, each item–user pair is associated to every co-cluster with some membership grade. This grade indicates the level of relevance of the item–user pair to the co-cluster. Furthermore, we propose a distributed algorithm, based on a map-reduce approach, to handle big datasets. Evaluating the fuzzy co-clustering algorithm on three datasets shows a significant improvement comparing with a regular co-clustering algorithm. In addition, a map-reduce version of the fuzzy co-clustering algorithm significantly reduces the runtime.

A group may appreciate recommendations on items that fit their joint preferences. When the members' actual preferences are unknown, a recommendation can be made with the aid of collaborative filtering methods. We offer to narrow down the recommended list of items by eliciting the users' actual preferences. Our final goal is to output top-k preferred items to the group out of the top-N recommendations provided by the recommender system (k

This article deals with a decision-making problem concerning an agent who wants to choose a partner from multiple candidates for long-term collaboration. To choose the best partner, the agent can rely on prior information he knows about the candidates. However, to improve his decision, he can request additional information from information sources. Nonetheless, acquiring information from external information sources about candidates may be biased due to different personalities of the agent searching for a partner and the information source. In addition, information may be costly. Considering the bias and the cost of the information sources, the optimization problem addressed in this article is threefold: (1) determining the necessary amount of additional information, (2) selecting information sources from which to request the information, and (3) choosing the candidates on whom to request the additional information. We propose a heuristic to solve this optimization problem. The results of experiments on simulated and real-world domains demonstrate the efficiency of our algorithm.

Many multi-agent settings require that agents identify appropriate partners or teammates with whom to work on tasks. When selecting potential partners, agents may benefit from obtaining information about the alternatives, for instance, through gossip (i.e., by consulting others) or reputation systems. When information is uncertain and associated with cost, deciding on the amount of information needed is a hard optimization problem. This article defines a statistical model, the Information-Acquisition Source Utility model (IASU), by which agents, operating in an uncertain world, can determine (1) which information sources they should request for information, and (2) the amount of information to collect about potential partners from each source. To maximize the expected gain from the choice, IASU computes the utility of choosing a partner by estimating the benefit of additional information. The article presents empirical studies through a simulation domain as well as a real-world domain of restaurants. We compare the IASU model to other relevant models and show that the use of the IASU model significantly increases agents' overall utility.

Accurately evaluating a researcher and the quality of his or her work is an important task when decision makers have to decide on such matters as promotions and awards. Publications and citations play a key role in this task, and many previous studies have proposed using measurements based on them for evaluating researchers. Machine learning techniques as a way of enhancing the evaluating process have been relatively unexplored. We propose using a machine learning approach for evaluating researchers. In particular, the proposed method combines the outputs of three learning techniques (logistics regression, decision trees, and artificial neural networks) to obtain a unified prediction with improved accuracy. We conducted several experiments to evaluate the model's ability to: (a) classify researchers in the field of artificial intelligence as Association for the Advancement of Artificial Intelligence (AAAI) fellows and (b) predict the next AAAI fellowship winners. We show that both our classification and prediction methods are more accurate than are previous measurement methods, and reach a precision rate of 96% and a recall of 92%.

Group Recommendation Systems (GRS) aim at recommending items that are relevant for the joint interest of a group of users. Voting mechanisms assume that users rate all items in order to identify an item that suits the preferences of all group members. This assumption is not feasible in sparse rating scenarios which are common in the recommender systems domain. In this paper we examine an application of voting theory to GRS. We propose a method to accurately determine the winning item while using a minimal set of the group members ratings, assuming that the recommender system has probabilistic knowledge about the distribution of users' ratings of items in the system. Since computing the optimal minimal set of ratings is computationally intractable, we propose two heuristic algorithms that proceed iteratively that aiming atto minimizing the number of required ratings, until identifying a "winning item". Experiments with the Netflix data show that the proposed algorithms reduce the required number of ratings for identifying the "winning item" by more than 50%.