Sharing Economy apps, such as Uber, Airbnb, and TaskRabbit, have generated a substantial consumer interest over the past decade. The unique form of peer-to-peer business exchange these apps have enabled has been linked to significant levels of economic growth, helping people in resource-constrained communities to build social capital and move up the economic ladder. However, due to the multidimensional nature of their operational environments, and the lack of effective methods for capturing and describing their end-users' concerns, Sharing Economy apps often struggle to survive. To address these challenges, in this paper, we examine crowd feedback in ecosystems of Sharing Economy apps. Specifically, we present a case study targeting the ecosystem of food delivery apps. Using qualitative analysis methods, we synthesize important user concerns present in the Twitter feeds and app store reviews of these apps. We further propose and intrinsically evaluate an automated procedure for generating a succinct model of these concerns. Our work provides a first step toward building a full understanding of user needs in ecosystems of Sharing Economy apps. Our objective is to provide Sharing Economy app developers with systematic guidelines to help them maximize their market fitness and mitigate their end-users' concerns and optimize their experience.

Information technology outsourcing (ITO) is a USD multi-trillion industry. There is growing competition among ITO service providers to improve their service deliveries. Natural language processing (NLP) is a technique, which can be leveraged to gain a competitive advantage in the ITO industry. This paper explores the information security implications of using NLP in ITO. First, it explores the use of NLP to enhance information security risk management (ISRM) in ITO. Then, it delves into the information security risks (ISRs) that may arise from the use of NLP in ITO. Finally, it proposes possible ISRM approaches to address those ISRs in ITO from the use of NLP. The study follows a qualitative approach using the case study method. Nine participants from three organisations (an ITO client, service provider and sub-contractor) engaged in an ITO relationship in the ICT industry were interviewed through a semi-structured questionnaire. The research findings were verified through a focus group. Case study scenarios are provided for a clear understanding of the findings. To the best of our knowledge, it is the first study to investigate the information security implications of the use of NLP in ITO.

In recent years, the number of smartphone users has increased dramatically. These users download millions of apps and use them for various services. Due to the significant demand for mobile apps, developers often seek faster development methods and more effective tools and techniques to generate these apps. Many of these apps are location-based apps in which users receive services based on their geographical location. In this paper, we propose a model-driven approach for the automatic generation of Android location-based mobile apps. Our framework, called ALBA, consists of a domain-specific modeling language, a modeling tool, and a plugin which includes model to code transformations. The modeling tool enables a novice designer to model a location-based app. The model is validated against the predefined constraints and the editor prevents creating invalid models. The designer uses the plugin to generate the Android code of the app. The evaluation of our work is two fold. First, to evaluate the generalizability of the ALBA framework, we conducted an experiment which includes the generation of four industrial location-based apps. Second, to evaluate the usability and quality of both the framework and the generated apps, we conducted a case study consists of three experiments. The results of the evaluation are promising both in terms of the applicability of the framework and the quality of the generated apps.

Textual documents produced in the software engineering process are a popular target for natural language processing (NLP) and information retrieval (IR) approaches. However, issue tickets often contain artifacts such as code snippets, log outputs and stack traces. These artifacts not only inflate the issue ticket sizes, but also can this noise constitute a real problem for some NLP approaches, and therefore has to be removed in the pre-processing of some approaches. In this paper, we present a machine learning based approach to classify textual content into natural language and non-natural language artifacts at line level. We show how data from GitHub issue trackers can be used for automated training set generation, and present a custom preprocessing approach for the task of artifact removal. The training sets are automatically created from Markdown annotated issue tickets and project documentation files. We use these generated training sets to train a Markdown agnostic model that is able to classify un-annotated content. We evaluate our approach on issue tickets from projects written in C++, Java, JavaScript, PHP, and Python. Our approach achieves ROC-AUC scores between 0.92 and 0.96 for language-specific models. A multi-language model trained on the issue tickets of all languages achieves ROC-AUC scores between 0.92 and 0.95. The provided models are intended to be used as noise reduction pre-processing steps for NLP and IR approaches working on issue tickets.

The development of IoT-based Android health monitoring mobile applications (apps) using traditional software development methods is a challenging task. Developers need to be familiar with various programming languages to manage the heterogeneity of hardware and software systems and to support different communication technologies. To address these problems, in this paper, we first analyze the domain of health monitoring mobile applications and then propose a framework based on model-driven engineering that accelerates the development of such systems. The proposed framework, called HealMA, includes a domain-specific modeling language, a graphical modeling editor, several validation rules, and a set of model-to-code transformations, all packed as an Eclipse plugin. We evaluated the framework to assess its applicability in generating various mobile health applications, as well as its impact on software productivity. To this end, four different health monitoring applications have been automatically generated. Then, we evaluated the productivity of software developers by comparing the time and effort it takes to use HealMA compared to a code-centric process. As part of the evaluation, we also evaluated the usability of HealMA-generated apps by conducting a user study. The results show that HealMA is both applicable and beneficial for automatic generation of usable IoT-based Android health monitoring apps.

In this paper, we present the latest version 3.0.0 of EvoMaster, an open-source search-based fuzzer aimed at Web APIs. We discuss and present all its recent improvements, including advanced white-box heuristics, advanced search algorithms, support for databases and external services, as well as dealing with GraphQL and RPC APIs besides the original use case for REST APIs. The tool's installers have been downloaded more than 3000 times. EvoMaster is in daily use for fuzzing millions of lines of code in hundreds of APIs in large Fortune 500 companies, such as for example the e-commerce Meituan.

Testing is an important task within software development. To write test cases and integrate them into an automated test suite requires a significant amount of work. Given a set of requirements and specifications of a software, testing is needed to verify its correctness. When done manually, it is an expensive and error prone task. To facilitate such work, automated test-case generation via tools could be useful. Test-case generation can be facilitated by deterministic algorithm-driven approaches or non-deterministic approaches such as with AI (e.g., evolutionary and LLM). The different approaches come with their strengths and weaknesses, which must be considered when integrating these approaches into a product test procedure in industry. Several novel testing techniques and tools have been developed in academia and industry, but how effective they are and how to integrate them in real-world large industrial scenarios is still unclear. In this paper, a systematic approach is presented to evaluate test-case generation methodologies and integrate them into a scalable enterprise setup. The specific context is black-box testing of REST APIs, based on their OpenAPI schemas. The aim is to facilitate IT product development and service delivery. The proposed Technology Adoption Performance Evaluation (TAPE) approach is evaluated by a case study within the Group IT of Volkswagen AG. We evaluated existing tools such as OpenAPI Generator, EvoMaster and StarCoder which are built on different technologies. Our results show that these tools are of benefit for test engineers to facilitate test-case specification and design within the Group IT of Volkswagen AG.

Ever since the first large language models (LLMs) have become available, both academics and practitioners have used them to aid software engineering tasks. However, little research as yet has been done in combining search-based software engineering (SBSE) and LLMs. In this paper, we evaluate the use of LLMs as mutation operators for genetic improvement (GI), an SBSE approach, to improve the GI search process. In a preliminary work, we explored the feasibility of combining the Java GI toolkit with OpenAI LLMs in order to generate an edit for the JCodec tool. Here we extend this investigation involving three LLMs and three types of prompt, and five real-world software projects. We sample the edits at random, as well as using local search. We also conducted a qualitative analysis to understand why LLM-generated code edits break as part of our evaluation. Our results show that, compared with conventional statement GI edits, LLMs produce fewer unique edits, but these compile and pass tests more often, with the OpenAI model finding test-passing edits 77% of the time. The OpenAI and Mistral LLMs are roughly equal in finding the best run-time improvements. Simpler prompts are more successful than those providing more context and examples. The qualitative analysis reveals a wide variety of areas where LLMs typically fail to produce valid edits commonly including inconsistent formatting, generating non-Java syntax, or refusing to provide a solution.