Commercial wearables from Fitbit, Garmin, and Whoop have recently introduced real-time notifications based on detecting changes in physiological responses indicating potential stress. In this paper, we investigate how these new capabilities can be leveraged to improve stress management. We developed a smartwatch app, a smartphone app, and a cloud service, and conducted a 100-day field study with 122 participants who received prompts triggered by physiological responses several times a day. They were asked whether they were stressed, and if so, to log the most likely stressor. Each week, participants received new visualizations of their data to self-reflect on patterns and trends. Participants reported better awareness of their stressors, and self-initiating fourteen kinds of behavioral changes to reduce stress in their daily lives. Repeated self-reports over 14 weeks showed reductions in both stress intensity (in 26,521 momentary ratings) and stress frequency (in 1,057 weekly surveys).

Accurate fault diagnosis, with immediate fault identification and isolation, is of paramount importance for power converters of switched reluctance motor drives, as it allows early adoption of fault tolerant procedures that eliminate the adverse effects of faults on machine operation. This paper presents an online fault diagnostic algorithm for power converter faults in SRM drives based on high frequency voltage signal injection. Unlike the other methods that use additional sensors, this algorithm extracts the fault signatures from the fundamental current by injecting a high frequency voltage signal into the upper switches of asymmetric power converter. The typical four fault types of power transistors are analyzed by monitoring the frequency and amplitude variation of the extracted high frequency current signal. In addition, the variation in amplitude of the fundamental current with the occurrence of fault is also considered as fault signature. Simulations performed with three phase 12/8 SRM drive and results are presented to verify the effectiveness of the proposed diagnostic algorithm.

Differential privacy concepts have been successfully used to protect anonymity of individuals in population-scale analysis. Sharing of mobile sensor data, especially physiological data, raise different privacy challenges, that of protecting private behaviors that can be revealed from time series of sensor data. Existing privacy mechanisms rely on noise addition and data perturbation. But the accuracy requirement on inferences drawn from physiological data, together with well-established limits within which these data values occur, render traditional privacy mechanisms inapplicable. In this work, we define a new behavioral privacy metric based on differential privacy and propose a novel data substitution mechanism to protect behavioral privacy. We evaluate the efficacy of our scheme using 660 hours of ECG, respiration, and activity data collected from 43 participants and demonstrate that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors.

Traceability is the only means to ensure that the source code of a system is consistent with its requirements and that all and only the specified requirements have been implemented by developers. During software maintenance and evolution, requirement traceability links become obsolete because developers do not/cannot devote effort to updating them. Yet, recovering these traceability links later is a daunting and costly task for developers. Consequently, the literature has proposed methods, techniques, and tools to recover these traceability links semi-automatically or automatically. Among the proposed techniques, the literature showed that information retrieval (IR) techniques can automatically recover traceability links between free-text requirements and source code. However, IR techniques lack accuracy (precision and recall). In this paper, we show that mining software repositories and combining mined results with IR techniques can improve the accuracy (precision and recall) of IR techniques and we propose Trustrace, a trust--based traceability recovery approach. We apply Trustrace on four medium-size open-source systems to compare the accuracy of its traceability links with those recovered using state-of-the-art IR techniques from the literature, based on the Vector Space Model and Jensen-Shannon model. The results of Trustrace are up to 22.7 percent more precise and have 7.66 percent better recall values than those of the other techniques, on average. We thus show that mining software repositories and combining the mined data with existing results from IR techniques improves the precision and recall of requirement traceability links.

Requirements traceability (RT) links help developers to understand programs and ensure that their source code is consistent with its documentation. Creating RT links is a laborious and resource-consuming task. Information Retrieval (IR) techniques are useful to automatically recover traceability links. However, IR-based approaches typically have low accuracy (precision and recall) and, thus, creating RT links remains a human intensive process. We conjecture that understanding how developers verify RT links could help improve the accuracy of IR-based approaches to recover RT links. Consequently, we perform an empirical study consisting of two controlled experiments. First, we use an eye-tracking system to capture developers' eye movements while they verify RT links. We analyse the obtained data to identify and rank developers' preferred source code entities (SCEs), e.g., class names, method names. Second, we use the ranked SCEs to propose two new weighting schemes called SE/IDF (source code entity/inverse document frequency) and DOI/IDF (domain or implementation/inverse document frequency) to recover RT links combined with an IR technique. SE/IDF is based on the developers preferred SCEs to verify RT links. DOI/IDF is an extension of SE/IDF distinguishing domain and implementation concepts. We use LSI combined with SE/IDF, DOI/IDF, and TF/IDF to show, using two systems, iTrust and Pooka, that LSIDOI/IDFstatistically improves the accuracy of the recovered RT links over LSITF/IDF.

Developers may introduce anti-patterns in their software systems because of time pressure, lack of understanding, communication, and--or skills. Anti-patterns impede development and maintenance activities by making the source code more difficult to understand. Detecting anti-patterns in a whole software system may be infeasible because of the required parsing time and of the subsequent needed manual validation. Detecting anti-patterns on subsets of a system could reduce costs, effort, and resources. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently some limitations: they require extensive knowledge of anti-patterns, they have limited precision and recall, and they cannot be applied on subsets of systems. To overcome these limitations, we introduce SVMDetect, a novel approach to detect anti-patterns, based on a machine learning technique---support vector machines. Indeed, through an empirical study involving three subject systems and four anti-patterns, we showed that the accuracy of SVMDetect is greater than of DETEX when detecting anti-patterns occurrences on a set of classes. Concerning, the whole system, SVMDetect is able to find more anti-patterns occurrences than DETEX.

Bug location assists developers in locating culprit source code that must be modified to fix a bug. Done manually, it requires intensive search activities with unpredictable costs of effort and time. Information retrieval (IR) techniques have been proven useful to speedup bug location in object-oriented programs. IR techniques compute the textual similarities between a bug report and the source code to provide a list of potential culprit classes to developers. They rank the list of classes in descending order of the likelihood of the classes to be related to the bug report. However, due to the low textual similarity between source code and bug reports, IR techniques may put a culprit class at the end of a ranked list, which forces developers to manually verify all non-culprit classes before finding the actual culprit class. Thus, even with IR techniques, developers are not saved from manual effort. In this paper, we conjecture that binary class relationships (BCRs) could improve the rankings by IR techniques of classes and, thus, help reducing developers' manual effort. We present an approach, LIBCROOS, that combines the results of any IR technique with BCRs gathered through source code analyses. We perform an empirical study on four programs -- Jabref, Lucene, muCommander, and Rhino -- to compare the accuracy, in terms of ranking, of LIBCROOS with two IR techniques: latent semantic indexing (LSI) and vector space model (VSM). The results of this empirical study show that LIBCROOS improves the rankings of both IR technique statistically when compared to LSI and VSM alone and, thus, may reduce the developers' effort.

Smart phones, gaming consoles, and wireless routers are ubiquitous; the increasing diffusion of such devices with limited resources, together with society's unsatiated appetite for new applications, pushes companies to miniaturize their programs. Miniaturizing a program for a hand-held device is a time-consuming task often requiring complex decisions. Companies must accommodate conflicting constraints: customers' satisfaction with features may be in conflict with a device's limited storage, memory, or battery life. This paper proposes a process, MoMS, for the multi-objective miniaturization of software to help developers miniaturize programs while satisfying multiple conflicting constraints. It can be used to support the reverse engineering, next release problem, and porting of both software and product lines. The process directs the elicitation of customer pre-requirements, their mapping to program features, and the selection of the features to port. We present two case studies based on Pooka, an email client, and SIP Communicator, an instant messenger, to demonstrate that MoMS supports optimized miniaturization and helps reduce effort by 77%, on average, over a manual approach.

Traceability is a task to create/recover traceability links among different software artifacts. It uses resources, such as an expert, source and target document, and traceability approach, to create/recover traceability links. However, it does not provide any guidance that how much we can trust on available resources. We propose Trustrace, a trust-based traceability recovery process, to improve expert trust on a recovered link and trust over the traceability inputs. Trustrace has three sub components, in particular, Link trust improver (LTI), traceability factor controller (TFC), and a hybrid traceability approach (HTA). LTI uses various source of information, such as temporal information, design documents, source code structure, and so on, to increase experts' trust over a link. To develop TFC, we will perform a systematic literature review and empirical studies to find out which factors impact the traceability-process inputs and document these factors in a trust pattern. TFC trust pattern will help practitioner and researchers to know which steps they can take to avoid/control these factors to improve their trust on these inputs. In the HTA, we will combine different traceability recovery approaches. All approaches have different positive and negative points, we will combine all the positive points of different approaches to increase experts' trust over the HTA. In Trustrace, HTA will implement the LTI model following TFC instructions to improve the expert trust over recovered link as well as precision and recall.

Information retrieval (IR) approaches have proven useful in recovering traceability links between free text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trustrace, inspired by Web trust models to improve the precision and recall of traceability links: Trustrace uses any traceability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, Histrace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with Histrace to build TrustraceVSM, Histracein which we use Histrace as one expert adding knowledge to the traceability links extracted from CVS/SVN change logs. We apply TrustraceVSM, Histraceon two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that TrustraceVSM, Histraceimproves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.

Searching for witnesses in case of road accidents is a challenging task for the police and involved persons. In this paper, we propose a mechanism that helps to find witnesses. Our solution preserves the potential witnesses' anonymity and gives them a free hand to decide whether to step forward as a witness or not. We analyze the performance of our application using a realistic model of a German city.