Showing papers by "University of Sannio published in 2015"

Characterization of the LIGO detectors during their sixth science run

Abstract: In 2009–2010, the Laser Interferometer Gravitational-Wave Observatory (LIGO) operated together with international partners Virgo and GEO600 as a network to search for gravitational waves (GWs) of astrophysical origin. The sensitivity of these detectors was limited by a combination of noise sources inherent to the instrumental design and its environment, often localized in time or frequency, that couple into the GW readout. Here we review the performance of the LIGO instruments during this epoch, the work done to characterize the detectors and their data, and the effect that transient and continuous noise artefacts have on the sensitivity of LIGO to a variety of astrophysical sources.

How can i improve my app? Classifying user reviews for software maintenance and evolution

Abstract: App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%).

Calibrating Probability with Undersampling for Unbalanced Classification

Abstract: Under sampling is a popular technique for unbalanced datasets to reduce the skew in class distributions. However, it is well-known that under sampling one class modifies the priors of the training set and consequently biases the posterior probabilities of a classifier. In this paper, we study analytically and experimentally how under sampling affects the posterior probability of a machine learning model. We formalize the problem of under sampling and explore the relationship between conditional probability in the presence and absence of under sampling. Although the bias due to under sampling does not affect the ranking order returned by the posterior probability, it significantly impacts the classification accuracy and probability calibration. We use Bayes Minimum Risk theory to find the correct classification threshold and show how to adjust it after under sampling. Experiments on several real-world unbalanced datasets validate our results.

When and why your code starts to smell bad

Abstract: In past and recent years, the issues related to managing technical debt received significant attention by researchers from both industry and academia. There are several factors that contribute to technical debt. One of these is represented by code bad smells, i.e., symptoms of poor design and implementation choices. While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smell-introducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smell-introducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.

Mining Version Histories for Detecting Code Smells

Abstract: Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase change- and fault-proneness. While most of the detection techniques just rely on structural information, many code smells are intrinsically characterized by how code elements change over time. In this paper, we propose H istorical I nformation for S mell de T ection (HIST), an approach exploiting change history information to detect instances of five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy. We evaluate HIST in two empirical studies. The first, conducted on 20 open source projects, aimed at assessing the accuracy of HIST in detecting instances of the code smells mentioned above. The results indicate that the precision of HIST ranges between 72 and 86 percent, and its recall ranges between 58 and 100 percent. Also, results of the first study indicate that HIST is able to identify code smells that cannot be identified by competitive approaches solely based on code analysis of a single system’s snapshot. Then, we conducted a second study aimed at investigating to what extent the code smells detected by HIST (and by competitive code analysis techniques) reflect developers’ perception of poor design and implementation choices. We involved 12 developers of four open source projects that recognized more than 75 percent of the code smell instances identified by HIST as actual design/implementation problems.

Mitochondrial uncoupling proteins and energy metabolism

Abstract: Understanding the metabolic factors that contribute to energy metabolism (EM) is critical for the development of new treatments for obesity and related diseases. Mitochondrial oxidative phosphorylation is not perfectly coupled to ATP synthesis, and the process of proton-leak plays a crucial role. Proton-leak accounts for a significant part of the resting metabolic rate (RMR) and therefore enhancement of this process represents a potential target for obesity treatment. Since their discovery, uncoupling proteins have stimulated great interest due to their involvement in mitochondrial-inducible proton-leak. Despite the widely accepted uncoupling/thermogenic effect of uncoupling protein one (UCP1), which was the first in this family to be discovered, the reactions catalyzed by its homolog UCP3 and the physiological role remain under debate. This review provides an overview of the role played by UCP1 and UCP3 in mitochondrial uncoupling/functionality as well as EM and suggests that they are a potential therapeutic target for treating obesity and its related diseases such as type II diabetes mellitus.

The Impact of API Change- and Fault-Proneness on the User Ratings of Android Apps

Abstract: The mobile apps market is one of the fastest growing areas in the information technology. In digging their market share, developers must pay attention to building robust and reliable apps. In fact, users easily get frustrated by repeated failures, crashes, and other bugs; hence, they abandon some apps in favor of their competition. In this paper we investigate how the fault- and change-proneness of APIs used by Android apps relates to their success estimated as the average rating provided by the users to those apps. First, in a study conducted on 5,848 (free) apps, we analyzed how the ratings that an app had received correlated with the fault- and change-proneness of the APIs such app relied upon. After that, we surveyed 45 professional Android developers to assess (i) to what extent developers experienced problems when using APIs, and (ii) how much they felt these problems could be the cause for unfavorable user ratings. The results of our studies indicate that apps having high user ratings use APIs that are less fault- and change-prone than the APIs used by low rated apps. Also, most of the interviewed Android developers observed, in their development experience, a direct relationship between problems experienced with the adopted APIs and the users’ ratings that their apps received.

User reviews matter! Tracking crowdsourced reviews to support evolution of successful apps

Abstract: Nowadays software applications, and especially mobile apps, undergo frequent release updates through app stores. After installing/updating apps, users can post reviews and provide ratings, expressing their level of satisfaction with apps, and possibly pointing out bugs or desired features. In this paper we show—by performing a study on 100 Android apps—how developers addressing user reviews increase their app's success in terms of ratings. Specifically, we devise an approach, named CRISTAL, for tracing informative crowd reviews onto source code changes, and for monitoring the extent to which developers accommodate crowd requests and follow-up user reactions as reflected in their ratings. The results indicate that developers implementing user reviews are rewarded in terms of ratings. This poses the need for specialized recommendation systems aimed at analyzing informative crowd reviews and prioritizing feedback to be satisfied in order to increase the apps success.

Are test smells really harmful? An empirical study

Abstract: Bad code smells have been defined as indicators of potential problems in source code. Techniques to identify and mitigate bad code smells have been proposed and studied. Recently bad test code smells (test smells for short) have been put forward as a kind of bad code smell specific to tests such a unit tests. What has been missing is empirical investigation into the prevalence and impact of bad test code smells. Two studies aimed at providing this missing empirical data are presented. The first study finds that there is a high diffusion of test smells in both open source and industrial software systems with 86 % of JUnit tests exhibiting at least one test smell and six tests having six distinct test smells. The second study provides evidence that test smells have a strong negative impact on program comprehension and maintenance. Highlights from this second study include the finding that comprehension is 30 % better in the absence of test smells.

Lab-on-fiber technology: a new vision for chemical and biological sensing

Abstract: The integration of microfluidics and photonic biosensors has allowed achievement of several laboratory functions in a single chip, leading to the development of photonic lab-on-a-chip technology. Although a lot of progress has been made to implement such sensors in small and easy-to-use systems, many applications such as point-of-care diagnostics and in vivo biosensing still require a sensor probe able to perform measurements at precise locations that are often hard to reach. The intrinsic property of optical fibers to conduct light to a remote location makes them an ideal platform to meet this demand. The motivation to combine the good performance of photonic biosensors on chips with the unique advantages of optical fibers has thus led to the development of the so-called lab-on-fiber technology. This emerging technology envisages the integration of functionalized materials on micro- and nano-scales (i.e. the labs) with optical fibers to realize miniaturized and advanced all-in-fiber probes, especially useful for (but not limited to) label-free chemical and biological applications. This review presents a broad overview of lab-on-fiber biosensors, with particular reference to lab-on-tip platforms, where the labs are integrated on the optical fiber facet. Light-matter interaction on the fiber tip is achieved through the integration of thin layers of nanoparticles or nanostructures supporting resonant modes, both plasmonic and photonic, highly sensitive to local modifications of the surrounding environment. According to the physical principle that is exploited, different configurations - such as localized plasmon resonance probes, surface enhanced Raman scattering probes and photonic probes - are classified, while various applications are presented in context throughout. For each device, the surface chemistry and the related functionalization protocols are reviewed. Moreover, the implementation strategies and fabrication processes, either based on bottom-up or top-down approaches, are discussed. In conclusion we highlight some of the further development opportunities, including lab-in-a-needle technology, which could have a direct and disruptive impact in localized cancer treatment applications.

Conceptualisation of patient satisfaction: a systematic narrative literature review.

Abstract: Aim:Patient satisfaction concept is widely measured due to its appropriateness to health service; however, evidence suggests that it is a poorly developed concept This article is a first part of a

Model Order Reductions for Stability Analysis of Islanded Microgrids With Droop Control

Abstract: Three-phase inverters subject to droop control are widely used in islanded microgrids to interface distributed energy resources to a network and to properly share loads among different units. In this paper, a mathematical model for islanded microgrids with linear loads and inverters under frequency and voltage droop control is proposed. The model is constructed by introducing a suitable state-space transformation that allows to write the closed-loop model in an explicit state-space form. Then, the singular perturbations technique is used to obtain reduced order models that reproduce the stability properties of the original closed-loop model. The analysis shows that the currents' dynamics influence the stability of the microgrid, particularly for high values of the frequency droop control parameters. It is also shown that a further reduction of the model order leads to a typical oscillator model that is not able to predict the possible instability of the droop-controlled system. Numerical and experimental results demonstrate the validity of the proposed models.

Detecting Android malware using sequences of system calls

Abstract: The increasing diffusion of smart devices, along with the dynamism of the mobile applications ecosystem, are boosting the production of malware for the Android platform. So far, many different methods have been developed for detecting Android malware, based on either static or dynamic analysis. The main limitations of existing methods include: low accuracy, proneness to evasion techniques, and weak validation, often limited to emulators or modified kernels. We propose an Android malware detection method, based on sequences of system calls, that overcomes these limitations. The assumption is that malicious behaviors (e.g., sending high premium rate SMS, cyphering data for ransom, botnet capabilities, and so on) are implemented by specific system calls sequences: yet, no apriori knowledge is available about which sequences are associated with which malicious behaviors, in particular in the mobile applications ecosystem where new malware and non-malware applications continuously arise. Hence, we use Machine Learning to automatically learn these associations (a sort of "fingerprint" of the malware); then we exploit them to actually detect malware. Experimentation on 20000 execution traces of 2000 applications (1000 of them being malware belonging to different malware families), performed on a real device, shows promising results: we obtain a detection accuracy of 97%. Moreover, we show that the proposed method can cope with the dynamism of the mobile apps ecosystem, since it can detect unknown malware.

Active edible coating effectiveness in shelf-life enhancement of trout (Oncorhynchusmykiss) fillets

Abstract: Carrageenan-based biocomposites have significant potential to develop packaging films and coatings for shelf-life extension of food products. In this study trout fresh fillets were separated into three groups: uncoated trout fillet (UTF), coated with carrageenan-based materials (coated trout fillet, CTF), and coated with carrageenan-based materials containing 1 g/100 mL essential lemon oil (active coated trout fillet, ACTF). The effect of carrageenan coating enriched with essential lemon oil on the quality of rainbow trout ( Oncorhynchusmykiss ) fillets during refrigerated storage (4 ± 1 °C) over a period of 15 days was evaluated. Trout fillets were analyzed for microbiological (total viable count, Enterobacteriacea counts, lactic acid bacteria, H 2 S-producing bacteria), chemical (TVB-N, moisture, pH), and biochemical (fatty acids content) characteristics. This study demonstrates the effectiveness of an edible active carrageenan coating in preserving fresh trout fillets from lipid oxidation and microbial growth.

How can I use this method

Abstract: Code examples are small source code fragments whose purpose is to illustrate how a programming language construct, an API, or a specific function/method works. Since code examples are not always available in the software documentation, researchers have proposed techniques to automatically extract them from existing software or to mine them from developer discussions. In this paper we propose muse (Method USage Examples), an approach for mining and ranking actual code examples that show how to use a specific method. muse combines static slicing (to simplify examples) with clone detection (to group similar examples), and uses heuristics to select and rank the best examples in terms of reusability, understandability, and popularity. muse has been empirically evaluated using examples mined from six libraries, by performing three studies involving a total of 140 developers to: (i) evaluate the selection and ranking heuristics, (ii) provide their perception on the usefulness of the selected examples, and (iii) perform specific programming tasks using the muse examples. The results indicate that muse selects and ranks examples close to how humans do, most of the code examples (82%) are perceived as useful, and they actually help when performing programming tasks.

How the Apache community upgrades dependencies: an evolutionary study

Abstract: Software ecosystems consist of multiple software projects, often interrelated by means of dependency relations. When one project undergoes changes, other projects may decide to upgrade their dependency. For example, a project could use a new version of a component from another project because the latter has been enhanced or subject to some bug-fixing activities. In this paper we study the evolution of dependencies between projects in the Java subset of the Apache ecosystem, consisting of 147 projects, for a period of 14 years, resulting in 1,964 releases. Specifically, we investigate (i) how dependencies between projects evolve over time when the ecosystem grows, (ii) what are the product and process factors that can likely trigger dependency upgrades, (iii) how developers discuss the needs and risks of such upgrades, and (iv) what is the likely impact of upgrades on client projects. The study results--qualitatively confirmed by observations made by analyzing the developers' discussion--indicate that when a new release of a project is issued, it triggers an upgrade when the new release includes major changes (e.g., new features/services) as well as large amount of bug fixes. Instead, developers are reluctant to perform an upgrade when some APIs are removed. The impact of upgrades is generally low, unless it is related to frameworks/libraries used in crosscutting concerns. Results of this study can support the understanding of the of library/component upgrade phenomenon, and provide the basis for a new family of recommenders aimed at supporting developers in the complex (and risky) activity of managing library/component upgrade within their software projects.

Improving Multi-Objective Test Case Selection by Injecting Diversity in Genetic Algorithms

Abstract: A way to reduce the cost of regression testing consists of selecting or prioritizing subsets of test cases from a test suite according to some criteria. Besides greedy algorithms, cost cognizant additional greedy algorithms, multi-objective optimization algorithms, and multi-objective genetic algorithms (MOGAs), have also been proposed to tackle this problem. However, previous studies have shown that there is no clear winner between greedy and MOGAs, and that their combination does not necessarily produce better results. In this paper we show that the optimality of MOGAs can be significantly improved by diversifying the solutions (sub-sets of the test suite) generated during the search process. Specifically, we introduce a new MOGA, coined as DIversity based Genetic Algorithm (DIV-GA), based on the mechanisms of orthogonal design and orthogonal evolution that increase diversity by injecting new orthogonal individuals during the search process. Results of an empirical study conducted on eleven programs show that DIV-GA outperforms both greedy algorithms and the traditional MOGAs from the optimality point of view. Moreover, the solutions (sub-sets of the test suite) provided by DIV-GA are able to detect more faults than the other algorithms, while keeping the same test execution cost.

Effectiveness of Opcode ngrams for Detection of Multi Family Android Malware

Abstract: With the wide diffusion of smartphones and their usage in a plethora of processes and activities, these devices have been handling an increasing variety of sensitive resources. Attackers are hence producing a large number of malware applications for Android (the most spread mobile platform), often by slightly modifying existing applications, which results in malware being organized in families. Some works in the literature showed that opcodes are informative for detecting malware, not only in the Android platform. In this paper, we investigate if frequencies of ngrams of opcodes are effective in detecting Android malware and if there is some significant malware family for which they are more or less effective. To this end, we designed a method based on state-of-the-art classifiers applied to frequencies of opcodes ngrams. Then, we experimentally evaluated it on a recent dataset composed of 11120 applications, 5560 of which are malware belonging to several different families. Results show that an accuracy of 97% can be obtained on the average, whereas perfect detection rate is achieved for more than one malware family.

Tourism satisfaction effect on general country image, destination image, and post-visit intentions

Abstract: The study analyzes the relationship between tourism satisfaction, cognitive and affective country image, destination image, and post-visit intentions. The proposed research model is tested with a s...

Environmental and economic assessment of fresh fruit supply chain through value chain analysis. A case study in chestnuts industry

Abstract: In recent years, both researches and practitioners have devoted attention to environmental sustainability of supply chain (SC), while firms have modified their marketing strategies highlighting green practices in productive and logistic processes among the features of their products. These behaviours move firms to require to their suppliers the adoption of green measures and practices to reduce environmental impacts within the entire SC. This paper presents the results on an exploratory case-based research on the SC of fresh chestnuts aimed to integrate environmental concepts in the value chain approach, with a concurrent evaluation of sustainability improvements and their economic impact. Within the value chain configuration, environmental KPIs are defined for the specific case study and a logistic environmental model is developed. Within the model, an evaluation of carbon footprint for this SC is proposed, along with its possible improvements. Results include the analysis of different improvement scenar...

Optimizing energy consumption of GUIs in Android apps: a multi-objective approach

Abstract: The wide diffusion of mobile devices has motivated research towards optimizing energy consumption of software systems— including apps—targeting such devices. Besides efforts aimed at dealing with various kinds of energy bugs, the adoption of Organic Light-Emitting Diode (OLED) screens has motivated research towards reducing energy consumption by choosing an appropriate color palette. Whilst past research in this area aimed at optimizing energy while keeping an acceptable level of contrast, this paper proposes an approach, named GEMMA (Gui Energy Multi-objective optiMization for Android apps), for generating color palettes using a multi- objective optimization technique, which produces color solutions optimizing energy consumption and contrast while using consistent colors with respect to the original color palette. An empirical evaluation that we performed on 25 Android apps demonstrates not only significant improvements in terms of the three different objectives, but also confirmed that in most cases users still perceived the choices of colors as attractive. Finally, for several apps we interviewed the original developers, who in some cases expressed the intent to adopt the proposed choice of color palette, whereas in other cases pointed out directions for future improvements