Lately, software development has become a predominantly online process, as more teams host and monitor their projects remotely. Sophisticated approaches employ issue tracking systems like Jira, predicting the time required to resolve issues and effectively assigning and prioritizing project tasks. Several methods have been developed to address this challenge, widely known as bug-fix time prediction, yet they exhibit significant limitations. Most consider only textual issue data and/or use techniques that overlook the semantics and metadata of issues (e.g., priority or assignee expertise). Many also fail to distinguish actual development effort from administrative delays, including assignment and review phases, leading to estimates that do not reflect the true effort needed. In this work, we build an issue monitoring system that extracts the actual effort required to fix issues on a per-project basis. Our approach employs topic modeling to capture issue semantics and leverages metadata (components, labels, priority, issue type, assignees) for interpretable resolution time analysis. Final predictions are generated by an aggregated model, enabling contributors to make informed decisions. Evaluation across multiple projects shows the system can effectively estimate resolution time and provide valuable insights.

Background. Open Source Software (OSS) is experiencing an increasing popularity both in industry and in academia. Aim. We investigated models for the selection, evaluation, and adoption of OSS, focusing on factors that affect most the evaluation of OSS. Method. We conducted a Systematic Literature Review of 262 studies published until the end of 2019, to understand whether OSS selection is still an interesting topic for researchers, and which factors are considered by stakeholders and are assessed by the available models. Result. We selected 60 primary studies: 20 surveys and 5 lessons learned studies elicited the motivations for OSS adoption; 35 papers proposed several OSS evaluation models focusing on different technical aspects. This Systematic Literature Review provides an overview of the available OSS evaluation methods, highlighting their limits and strengths, based on the wide range of technicalities and aspects explored by the selected primary studies. Conclusion. OSS producers can benefit from our results by checking if they are providing all the information commonly required by potential adopters. Users can learn how models work and which models cover the relevant characteristics of OSS they are most interested in.

Abstract Functional size measures are used in several software development processes, because they can be applied as soon as the functional user requirements of a software application have been specified. Quite often, functional user requirements are used as the main input of effort and cost estimation. Since effort and cost estimates are fundamental for decision making, project managers would like to get reasonably accurate estimates even before user requirements have been specified completely and at the proper level of detail. To this end, early size estimation methods have been proposed. In this paper, we consider a specific situation; namely, we consider processes in which the requirements elicitation and modeling phases are carried out according to the ICONIX process. The ICONIX process involves a few stages, each one tackling an aspect of requirements and delivering a specific artifact, in the form of a UML model. We explore the possibility of deriving from ICONIX artifacts the information needed to estimate the final size of the application. To this end, we analyzed the requirements of 21 software applications and derived statistical models that correlate the information extracted from ICONIX artifacts to the size, expressed in function points, of the application. The analysis of the achieved estimates shows that the approach is viable, in that it provides early estimates with good accuracy.

Background: Technical debt is currently receiving increasing attention from practitioners and researchers. Several metaphors, concepts, and indications concerning technical debt have been introduced, but no agreement exists about a solid definition of technical debt.

Efficient energy planning is a key feature for the future smart cities. The real-time optimization of the energy distribution and storage is the real added value for smart grid and cities. However, the available energy providers' infrastructures are not able to estimate and predict real-time fluctuation of the energy demand and are not scalable enough to integrate, with low cost and effort, hardware elements able to estimate energy demand in real-time. The solution proposed in this paper exploit heterogeneous big data sources to forecast in real-time energy demands without requiring physical interventions on the energy providers' infrastructures. The proposed approach is mainly based on the use of statistical models and cellular network big data to estimate in advance energy demand without observing the actual behaviour of the energy network. Distributor System Operators can use these estimations to self-manage the energy demand, distribution and storage in real-time, without any user intervention. The approach has been extensively validated in a real world case study for the Milan city, in the production infrastructure of Vodafone Italy and with all the Vodafone Mobile Users, and the quality of the probabilistic models in forecasting energy consumption is really promising.

Service-oriented applications do not fully benefit from standard APIs yet, and many applications fail to use interchangeably all the services that implement a standard service API. This article presents an approach to develop adaptation strategies that improve service interchangeability for service-oriented applications based on standard APIs. In our approach, an adaptation strategy consists of sets of parametric adaptation plans (called test-and-adapt plans), which execute test cases to reveal the occurrence of interchangeability problems, and activate runtime adaptors according to the test results. Throughout this article, we formalize the structure of the parametric test-and-adapt plans and of their execution semantics, present an algorithm for identifying correct execution orders through sets of test-and-adapt plans, provide empirical evidence of the occurrence of interchangeability problems for sample applications and services, and discuss the effectiveness of the approach in terms of avoided failures, runtime overheads and development costs.

The quantification of several software attributes (e.g., size, complexity, cohesion, coupling) is usually carried out in a static fashion, and several hundreds of measures have been defined to this end. However, static measurement may only be an approximation for the measurement of these attributes during software use. The paper proposes a theoretical framework based on Axiomatic Approaches for the definition of sensible dynamic software measures that can dynamically capture these attributes. Dynamic measures based on this framework are defined for dynamically quantifying size and coupling. In this paper, we also compare dynamic measures of size and coupling against well-known static measures by correlating them with fault-pronenesses of four case studies.

Background. Open Source Software (OSS) provides increasingly serious and viable alternatives to traditional closed source software. The number of OSS users is continuously growing, as is the number of potential users that are interested in evaluating the quality of OSS. The latter would greatly benefit from simple methods for evaluating the trustworthiness of OSS.

Many modern software systems dynamically discover and integrate third party libraries, components and services that comply with standard APIs. Compliance with standard APIs facilitates dynamic binding, but does not always guarantee full behavioral compatibility. For instance, problems that derive from behavior incompatibility are quite frequent in service-oriented applications that dynamically bind service implementations that match API specifications.

Today, mobile learning has become the heir of electronic learning. M-learning provides a new learning experience, even though it introduces new research challenges and opens issues which need to be faced. This paper proposes a new platform based on the concepts of push services, instant messaging, and SIP (Session Initiation Protocol). The platform addresses the actual conceptual and technological limitations of existing m-learning solutions and it supports our new learning paradigm anytime + anywhere + just for me.

Mobile code solutions can improve the performances of applications over the Internet by reducing the amount of data that must be transferred across the network. The research on genome examines enormous amount of data stored on many distributed databases to retrieve the information relevant for the specific investigation, and thus is likely to greatly benefit for mobile code solutions. This paper studies the benefits of mobile code solutions for research on genome, by comparing different solutions with analytical models, and presenting the results of case studies that allow to identify when mobile code overwhelm client/server solutions. The measured improvements of performance indicate that the retrieval of information from genome databases is a promising domain for mobile code applications.

Autonomic communication and autonomic computing can solve many problems in managing complex network and computer systems, as well as network applications, where computing and networking coexist. Autonomic applications must be able to diagnose and repair their own faults automatically. In particular, they must be able to monitor the execution state, understand the behavior of the application and of the executing environment, and interpret monitored data to identify faults and select a repairing strategy. Assertions have been extensively studied in software engineering for identifying deviations from the expected behaviors and thus signal anomalous outcomes. Unfortunately, classic assertions are defined statically at development time and cannot capture unpredictable changes and evolutions in the execution environment. Thus, they do not easily adapt to autonomic applications. The paper proposes a method for the automatic synthesis and adaptation of assertions from the observed behavior of an application, aimed at achieving adaptive application monitoring. We believe that this represents an important basis to derive autonomic mechanisms that can deal with unpredictable situations.