Analytics

The App Store introduced a title-length limit for mobile apps in 2016, and similar policies were later adopted across the industry. This issue drew considerable attention from industry practitioners in the 2010s. Using both empirical and theoretical approaches, this paper examines the effectiveness of this policy and its welfare implications. Title length became an issue because some sellers assemble meaningful keywords in the app title to convey information to consumers, while others combine irrelevant yet popular keywords in an attempt to increase their app’s downloads. We hypothesize that when titles are short, title length is positively associated with an app’s performance because both honest and opportunistic sellers coexist in the market. However, due to the presence of opportunistic sellers, once titles become too long, this positive relationship disappears. We examine this hypothesis using a random sample of 1998 apps from the App Store in 2015. Our results show that for apps with titles longer than 30 characters, title length remains positively associated with app performance. However, for titles exceeding 50 characters, we do not have sufficient evidence to conclude that further increases in length continue to generate additional downloads. To interpret our empirical findings, we construct communication games between an app seller and a consumer, in which the equilibrium is characterized by a threshold. Based on our model and empirical observations, the 30-character limit might hurt consumers.

To support novice learners, the Java programming learning assistant system (JPLAS) has been developed with various features. Among them, code writing problem (CWP) assigns writing an answer code that passes a given test code. The correctness of an answer code is validated by running it on JUnit. In previous works, we implemented a code plagiarism checking function that calculates the similarity score for each pair of answer codes based on the Levenshtein distance. When the score is higher than a given threshold, this pair is regarded as plagiarism. However, a method for finding the proper threshold has not been studied. In addition, AI-generated codes have become threats in plagiarism, as AI has grown in popularity, which should be investigated. In this paper, we propose a threshold selection method based on Tukey’s IQR fences. It uses a custom upper threshold derived from the statistical distribution of similarity scores for each assignment. To better accommodate skewed similarity distributions, the method introduces a simple percentile-based adjustment for determining the upper threshold. We also design prompts to generate answer codes using generative AI and apply them to four AI models. For evaluation, we used a total of 745 source codes of two datasets. The first dataset consists of 420 answer codes across 12 CWP instances from 35 first-year undergraduate students in the State Polytechnic of Malang, Indonesia (POLINEMA). The second dataset includes 325 answer codes across five CWP assignments from 65 third-year undergraduate students at Okayama University, Japan. The applications of our proposals found the following: (1) any pair of student codes whose score is higher than the selected threshold has some evidence of plagiarism, (2) some student codes have a higher similarity than the threshold with AI-generated codes, indicating the use of generative AI, and (3) multiple AI models can generate code that resembles student-written code, despite adopting different implementations. The validity of our proposal is confirmed.

Global optimization is a fundamental tool for addressing complex and nonlinear problems across scientific and technological domains. The primary objective of this work is to enhance the efficiency, stability, and convergence speed of the Magnificent Frigatebird Optimization (MFO) algorithm by introducing new strategies that strengthen both global exploration and local exploitation. To this end, we propose an improved version of MFO that incorporates three novel movement strategies (aggressive, conservative, and mixed), a BFGS-based local search procedure for more accurate solution refinement, and a dynamic termination criterion capable of detecting stagnation and reducing unnecessary function evaluations. The algorithm is extensively evaluated on a diverse set of benchmark functions, demonstrating substantially lower computational cost and higher reliability compared to classical evolutionary and swarm-based methods. The results confirm the effectiveness of the proposed modifications and highlight the potential of the enhanced MFO for application to demanding real-world optimization problems.