Modeling in machine learning (ML) is becoming an essential part of software systems in practice. Validating ML applications is a challenging and time-consuming process for developers since the accuracy of prediction heavily relies on generated models. ML applications are written by relatively more data-driven programming based on the blackbox of ML frameworks. If all of the datasets and the ML application need to be individually investigated, the ML debugging tasks would take a lot of time and effort. To address this limitation, we present a novel debugging technique for machine learning applications, called MLDBUG that helps ML application developers inspect the training data and the generated features for the ML model. Inspired by software debugging for reproducing the potential reported bugs, MLDBUG takes as input an ML application and its training datasets to build the ML models, helping ML application developers easily reproduce and understand anomalies on the ML application. We have implemented an Eclipse plugin for MLDBUG which allows developers to validate the prediction behavior of their ML applications, the ML model, and the training data on the Eclipse IDE. In our evaluation, we used 23,500 documents in the bioengineering research domain. We assessed the MLDBUG's capability of how effectively our debugging technique can help ML application developers investi-gate the connection between the produced features and the labels in the training model and the relationship between the training instances and the instances the model predicts.

In this paper, we present a Learning To Rank-based approach that helps EXPLORE and understand Relevant documents for bioengineering text corpora, called LTREXPLORER. Based on the likelihood of being the most relevance to a search query, the ranking model sorts documents according to their degrees of relevance, preference, or importance with various domain-specific features. The evaluation results demonstrated that our approach has the potential to effectively provide the retrieval scoring functions to researchers, who focus on the most relevant documents in bioengineering information retrieval.

Reporting bugs is one of the vital activities for evolving software systems. Given such reports, developers cope with unanticipated behaviors during software development, maintenance, and operations. The description of bug reports typically includes (1) what errors occurred previously and (2) how a failure can be reproduced through specific steps, test inputs, and original configurations when a failure was created. However, analyzing bug reports is a tedious and error-prone process due to overflowing, complex terminologies. For example, diverse terms are used to represent similar or divergent elucidations by surrounding contexts during software development and maintenance. To address this problem, we present an approach that applies a topic mining technique to bug reports for finding an adequate code reviewer, who can potentially cope with reported failures, by inferring some hidden topics of a textual document.

Developers often make recurring changes, similar but different changes across multiple locations. They inspect such code changes per source file (i.e., a diff patch) during code reviews; however, diff patches represent low-level code modification without summarizing recurring changes, leading to tedious and error-prone code inspection. To address this problem, we propose a novel code review approach, Recurring Code Changes Inspection with Deep Learning (RIDL) that leverages change patterns of an edit script by learning code clones, identical or nearly similar code fragments. To train a classifier, RIDL learns 13,940 clones with four different clone types (e.g., Type-1, Type-2, Type-3, and Type-4 clones) from a clone database mined from 25,000 subject programs. Our approach then leverages the classifier to (1) interactively summarize recurring changes and (2) detect change mistakes, potential anomalies in a given codebase. In the evaluation, after 2 hours of training, RIDL analyzes code changes in four open source projects. It summarizes recurring changes with 95.1% accuracy and detects change anomalies with 93.1% accuracy. Our results show that RIDL should help developers effectively inspect recurring changes during code reviews.