Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract Developer expertise is a critical component of community-based question-answering platforms like Stack Overflow. However, expertise is not monolithic. Developers exhibit different "shapes" of expertise, such as deep specialists (I-shaped) or broad generalists with one specialty (T-shaped). While prior research has examined developer expertise and answer quality independently, how different expertise structures influence preferences for specific answer characteristics remains insufficiently understood. This paper investigates the relationship between a developer's expertise shape and their preference for specific answer characteristics.

We present a large-scale empirical study of over 48,000 Stack Overflow users, classifying them into I-shaped, T-shaped, Pi-shaped, and Comb-shaped profiles based on the distribution of tag-level reputation, following established expertise-shape modeling approaches. We then analyze the types of answers these user profiles tend to upvote and accept as solutions, focusing on characteristics such as answer length, inclusion of code snippets, use of images, and citation of external references.

Using separate analyses for community-level (upvotes) and task-resolution-level (accepted answers) preference signals, our findings reveal distinct and systematic differences across expertise shapes. I-shaped specialists favor technically deep, code-heavy answers, while T-shaped and Comb-shaped experts show a preference for more summarized, conceptual answers that include diagrams or references. These patterns are consistent across robustness checks and sensitivity analyses.

The results highlight that answer usefulness is user-dependent rather than universal, and they can help improve expertise-aware answer recommendation systems and foster more effective knowledge sharing on collaborative platforms.

Abstract Spectrum-Based Fault Localization (SBFL) techniques are widely used and studied. These techniques are straightforward, low-cost, and fast in comparison to other fault localization techniques, such as Mutation-Based and Learning-Based ones. However, the accuracy of these techniques is always controversial. The main criticism of these techniques refers to the mere use of coverage and spectra information. Hence, this study seeks to improve SBFL techniques by enhancing their accuracy while maintaining simplicity, aiming to make them applicable for regression faults. To achieve this, the first step involves integrating SBFL techniques with two features: (1) Change and (2) Test Case Weight, which utilizes the same coverage and spectrum data in a novel way. Additionally, the study examines the impact of incorporating Data and Control Dependency into the formula for fault localization accuracy. It also considers the suspiciousness scores generated by SBFL formulae as a feature.

Three SBFL techniques—Tarantula, Ochiai, and Jaccard—are applied in this study both as a feature and as a baseline for result comparison. The findings reveal that combining SBFL suspiciousness scores with Change and Test Case Weight significantly enhances performance by identifying at least 27% more faults at the Top-3 ranking. Furthermore, integrating these features with Data Dependency leads to even greater improvements, locating minimum 45% more faults at the Top-3 ranking compared to aforementioned SBFL formulae. Overall, this study emphasizes the limitations of existing SBFL formulae while highlighting the advantages of augmenting SBFL with additional features and repurposing spectral information to achieve greater accuracy in fault localization.

Abstract Process mining is a new research area that bridges the gap between data science and process science. Among the subfields of process mining, predictive process monitoring aims to predict process features such as the next event, outcome, and remaining time. In recent years, increasing research has been conducted in the area of remaining time prediction.
In this paper, we present an interpretable method for remaining time prediction. Interpretability is an advantageous feature for a prediction method, because it provides the necessary advice and warnings to the organization’s experts during the process execution when it is used in a recommender system. In the proposed method, we utilize distance metric learning methods to develop a distance function for process events. The distance function can be used to find the most similar cases to the intended case, and then the remaining time of the similar cases is used as the indicator of the remaining time of the intended case. Our proposed method has been evaluated on three datasets, and the evaluations revealed that it significantly outperforms the state-of-the-art baseline in two datasets while achieving comparable accuracy in the third dataset. Additionally, our proposed method provides interpretation, while the competitor methods are not interpretable.

Abstract Augmented reality technology integrates information with the environment in real-time. The capability to add information to objects and interact with users according to their requests can be an enjoyable and useful experience. Most Augmented Reality systems have a fixed static database containing registered objects. To enhance Augmented Reality applications, dynamic information sources can be used. An information source such as the web which has expanded and up-to-date data, could be an appropriate alternative. Using these information sources in real-time enables the use of Augmented Reality on a wider scale.
This paper introduces an Augmented Reality model based on online content is presented. In this research, case studies are human faces and context-based information is a virtual element, i.e., information about the target face. Using web information resources dynamizes the system in a variety of environments. Furthermore, the user interacts with the system through question-answering. Deep learning methods have been used for information retrieval and question-answering. Moreover, Answers to frequently asked questions that are confirmed by users are stored in the database for faster response time.
The experimental results reveal that users can interact with the Augmented Reality system through question-answering and augment the required information with a speed of 0.642 seconds and an F-score of 87.9% on the SQuAD dataset.