Intelligent user interfaces (IUI) are driven by the goal of improvement in human–computer interaction (HCI), mainly improving user interfaces’ user experience (UX) or usability with the help of artificial intelligence. The main goal of this study is to find, assess, and synthesize existing state-of-the-art work in the field of IUI with an additional focus on the evaluation of IUI. This study analyzed 211 studies published in the field between 2012 and 2022. Studies are most frequently tied to HCI and SE domains. Definitions of IUI were observed, showing that adaptation, representation, and intelligence are key characteristics associated with IUIs, whereas adaptation, reasoning, and representation are the most commonly used verbs in their description. Evaluation of IUI is mainly conducted with experiments and questionnaires, though usability and UX are not considered together in evaluations. Most evaluations (81% of studies) reported partial or complete improvement in usability or UX. A shortage of evaluation tools, methods, and metrics, tailored for IUI, is noticed. Most often, empirical data collection methods and data sources in IUI evaluation studies are experiment, prototype development, and questionnaire.

During maintenance, software systems undergo continuous correction and enhancement activities due to emerging faults, changing environments, and evolving requirements, making this phase expensive and time-consuming, often exceeding the initial development costs. To understand and manage software under development and maintenance better, several maintainability measures have been proposed. The Maintainability Index is commonly used as a quantitative measure of the relative ease of software maintenance. There are several Index variants that differ in the factors affecting maintainability (e.g., code complexity, software size, documentation) and their given importance. To explore the variants and understand how they compare when evaluating software maintainability, an experiment was conducted with 45 Java-based object-oriented software systems. The results showed that the choice of the variant could influence the perception of maintainability. Although different variants presented different values when subjected to the same software, their values were strongly positively correlated and generally indicated similarly how maintainability evolved between releases and over the long term. Though, when focusing on fine-grained results posed by the Index, the variant selection had a larger impact. Based on their characteristics, behavior, and interrelationships, the variants were divided into two distinct clusters, i.e., variants that do not consider code comments in their calculation and those that do.

Web performance testing with tools such as Google Lighthouse is a common task in software practice and research. However, variability in time-based performance measurement results is observed quickly when using the tool, even if the website has not changed. This can occur due to variability in the network, web, and client devices. In this paper, we investigated how this challenge was addressed in the existing literature. Furthermore, an experiment was conducted, highlighting how unrepresentative measurements can result from single runs; thus, researchers and practitioners are advised to run performance tests multiple times and use an aggregation value. Based on the empirical results, 5 consecutive runs using a median to aggregate results reduce variability greatly, and can be performed in a reasonable time. The study’s findings alert to p otential pitfalls when using single run-based measurement results and serve as guidelines for future use of the tool.

A common practice in software development is to include linters, static analysis tools that warn developers about potential issues in the code, in the software quality assurance process. Actionable warnings generated by linters upon violations of defined rules help detect, resolve, and reduce coding errors, quality flaws, code style inconsistencies, and deviations from best coding practices and conventions. However, little empirical evidence exists to fully understand the relationship between linter warnings and external software quality factors. To this end, an empirical investigation of the source code of 40 open-source JavaScript project releases was conducted to study whether there is a relation between software maintainability measured by the Maintainability Index and the density of linter warnings per Logical Lines of Code. The findings suggest a very weak to strong negative correlation between warning density and the value of the Index at a project- and a module-level. Changes in warning density between projects only slightly inversely correspond to changes in maintainability. Additionally, a statistically significant difference in maintainability was found between projects defining linters in their manifest file and those that do not, in favor of the former.

Software evolution is driven by changes made during software development and maintenance. While source control systems effectively manage these changes at the commit level, the intent behind them are often inadequately documented, making understanding their rationale challenging. Existing commit intent classification approaches, largely reliant on commit messages, only partially capture the underlying intent, predominantly due to the messages’ inadequate content and neglect of the semantic nuances in code changes. This paper presents a novel method for extracting semantic features from commits based on modifications in the source code, where each commit is represented by one or more fine-grained conjoint code changes, e.g., file-level or hunk-level changes. To address the unstructured nature of code, the method leverages a pre-trained transformer-based code model, further trained through task-adaptive pre-training and fine-tuning on the downstream task of intent classification. This fine-tuned task-adapted pre-trained code model is then utilized to embed fine-grained conjoint changes in a commit, which are aggregated into a unified commit-level vector representation. The proposed method was evaluated using two BERT-based code models, i.e., CodeBERT and GraphCodeBERT, and various aggregation techniques on data from open-source Java software projects. The results show that the proposed method can be used to effectively extract commit embeddings as features for commit intent classification and outperform current state-of-the-art methods of code commit representation for intent categorization in terms of software maintenance activities undertaken by commits.