Abstract Digital skills are crucial for career adaptability and long-term labour market outcomes, yet identifying interventions that deliver sustained benefits while ensuring equity remains challenging. To address this, we propose CausalSkillNet-RL, a fairness-aware causal reinforcement learning framework for personalised intervention recommendations. The framework combines for the first time (i) a novel fairness-aware reinforcement learning agent that optimises both immediate uplift and long-term skill growth while enforcing equity for protected groups; and (ii) a fairness-regularised DragonNet for principled estimation of heterogeneous treatment effects, to provide robust causal signals for policy learning. We evaluate CausalSkillNet-RL firstly on synthetic and real-world datasets (Stack OverFlow Survey, ClickStart), using heterogeneous effect metrics, ablation studies, and visualisation. Results show that it achieves higher average treatment effects, improved Uplift@K, and reduced fairness gaps compared to state-of-the-art baselines. These findings highlight the framework’s potential to support equitable, data-driven long-term interventions in digital skill development.

In this paper, we study the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. Through developer interviews and questionnaires, we uncover findings that highlight and quantify the effects of three high-level app store themes: bridging the gap between developers and users, increasing market transparency and affecting mobile release management. Our findings have implications for testing, requirements engineering and mining software repositories research fields. These findings can help guide future research in supporting mobile app developers through a deeper understanding of the app store-developer interaction.

Code-free software similarity detection techniques have been used to support different software engineering tasks, including clustering mobile applications (apps). The way of measuring similarity may affect both the efficiency and quality of clustering solutions. However, there has been no previous comparative study of feature extraction methods used to guide mobile app clustering. In this paper, we investigate different techniques to compute the similarity of apps based on their textual descriptions and evaluate their effectiveness using hierarchical agglomerative clustering. To this end we carry out an empirical study comparing five different techniques, based on topic modelling and keyword feature extraction, to cluster 12,664 apps randomly sampled from the Google Play App Store. The comparison is based on three main criteria: silhouette width measure, human judgement and execution time. The results of our study show that using topic modelling, in addition to collocation-based and dependency-based feature extractors perform similarly in detecting app-feature similarity. However, dependency-based feature extraction performs better than any other in finding application domain similarity ( ρ = 0.7, p − v a l u e < 0.01). Current categorisation in the app store studied does not exhibit a good classification quality in terms of the claimed feature space. However, a better quality can be achieved using a good feature extraction technique and a traditional clustering method.

Context: Categorising software systems according to their functionality yields many benefits to both users and developers. Goal: In order to uncover the latent clustering of mobile apps in app stores, we propose a novel technique that measures app similarity based on claimed behaviour. Method: Features are extracted using information retrieval augmented with ontological analysis and used as attributes to characterise apps. These attributes are then used to cluster the apps using agglomerative hierarchical clustering. We empirically evaluate our approach on 17,877 apps mined from the BlackBerry and Google app stores in 2014. Results: The results show that our approach dramatically improves the existing categorisation quality for both Blackberry (from 0.02 to 0.41 on average) and Google (from 0.03 to 0.21 on average) stores. We also find a strong Spearman rank correlation (ρ= 0.96 for Google and ρ= 0.99 for BlackBerry) between the number of apps and the ideal granularity within each category, indicating that ideal granularity increases with category size, as expected. Conclusions: Current categorisation in the app stores studied do not exhibit a good classification quality in terms of the claimed feature space. However, a better quality can be achieved using a good feature extraction technique and a traditional clustering method.

Social software is built around an "architecture of participation" where user data is aggregated as a side-effect of using Web 2.0 applications. Web 2.0 implies that processes and tools are socially open, and that content can be used in several different contexts. Web 2.0 tools and technologies support interactive information sharing, data interoperability and user centered design. For instance, wikis, blogs, tags and feeds help us organize, manage and categorize content in an informal and collaborative way. Some of these technologies have made their way into collaborative software development processes and development platforms. These processes and environments are just scratching the surface of what can be done by incorporating Web 2.0 approaches and technologies into collaborative software development. Web 2.0 opens up new opportunities for developers to form teams and collaborate, but it also comes with challenges for developers and researchers. Web2SE aims to improve our understanding of how Web 2.0, manifested in technologies such as mashups or dashboards, can change the culture of collaborative software development.

Many companies are still asking how to assess and predict the maintenance cost of their software. Measures of software maintenance cost can be taken either late or early in the development process. Early measures of software maintenance cost are beneficial because they can help in allocating project resources efficiently, predicting the effort of maintenance tasks and controlling the maintenance process. This paper describes a tool for computing early metrics from UML class diagrams based on the Web Application Extension (WAE) for UML. A case study is used to show the usefulness and effectiveness of the tool.

Many web applications have evolved from simple HTML pages to complex applications that have a high maintenance cost. This high maintenance cost is due to the heterogeneity of web applications, to fast Internet evolution and the fast- moving market which imposes short development cycles and frequent modifications. In order to control the maintenance cost, quantitative metrics for predicting web applications maintainability must be used. This paper provides an exploratory study for new design metrics used for measuring the maintainability of web applications from class diagrams. The metrics are based on Web Application Extension (WAE)for UML and will measure the following design attributes: size, complexity, coupling and reusability. In this study the metrics are applied to two web applications from the telecommunications domain.

Operational software is constantly evolving. Regression testing is used to identify the unintended consequences of evolutionary changes. As most changes affect only a small proportion of the system, the challenge is to ensure that the regression test set is both safe (all relevant tests are used) and inclusive (only relevant tests are used). Previous approaches to reducing test sets struggle to find safe and inclusive tests by looking only at the changed code. We use decomposition program slicing to safely reduce the size of regression test sets by identifying those parts of a system that could not have been affected by a change; this information will then direct the selection of regression tests by eliminating tests that are not relevant to the change. The technique properly accounts for additions and deletions of code. We extend and use Rothermel and Harrold's framework for measuring the safety of regression test sets and introduce new safety and precision measures that do not require a priori knowledge of the exact number of modification-revealing tests. We then analytically evaluate and compare our techniques for producing reduced regression test sets.

Web applications incorporate important business assets and offer a convenient way for businesses to promote their services through the internet. \nMany of these web applications have evolved from simple HTML pages to complex applications that have high maintenance cost. The high maintenance cost of web applications is due to the inherent characteristics of web applications, to the fast internet evolution and to the pressing market which imposes short development cycles and frequent modifications. \nIn order to control the maintenance cost, quantitative metrics and models for predicting web applications' maintainability must be used. Since, web applications are different from traditional software systems, models and metrics for traditional systems can not be applied to web applications. The reason for that is that web applications have special features such as hypertext structure, dynamic code generation and heterogenousity that can not be captured by traditional and object-oriented metrics. In this paper, we will provide a comparative analysis of the different approaches for predicting web applications'

Abstract Recent software maintenance models have included impact analysis and accounting for ripple effect as one of their stages. This paper describes and explains the reformulation of Yau and Collofello's ripple‐effect algorithm and its validity within the software‐maintenance process. Completely automatic computation of ripple effect has until now proved troublesome; we show how our approximation algorithm helps to overcome this. Our Ripple Effect and Stability Tool (REST) which uses our approximated algorithm to compute ripple effect for C programs, is described. Eleven C programs are used in an initial investigation into whether our approximated algorithm can replace Yau and Collofello's original algorithm for the purpose of automatic computation of ripple effect. The Pearson correlation coefficient for the two versions of the algorithm across the eleven programs shows a high correlation. Copyright © 2001 John Wiley & Sons, Ltd.