Automating Requirements Traceability: Two Decades of Learning from KDD

This paper summarizes our experience with using Knowledge Discovery in Data (KDD) methodology for automated requirements tracing, and discusses our insights.Comment: The work of the second author has been supported in part by NSF grants CCF-1511117 and CICI 1642134; 4 pages; in Proceedings of IEEE Requirements Engineering 201

Assessing Traceability of Software Engineering Artifacts

The generation of traceability links or traceability matrices is vital to many software engineering activities. It is also person-power intensive, time-consuming, error-prone, and lacks tool support. The activities that require traceability information include, but are not limited to, risk analysis, impact analysis, criticality assessment, test coverage analysis, and verification and validation of software systems. Information Retrieval (IR) techniques have been shown to assist with the automated generation of traceability links by reducing the time it takes to generate the traceability mapping. Researchers have applied techniques such as Latent Semantic Indexing (LSI), vector space retrieval, and probabilistic IR and have enjoyed some success. This paper concentrates on examining issues not previously widely studied in the context of traceability: the importance of the vocabulary base used for tracing and the evaluation and assessment of traceability mappings and methods using secondary measures. We examine these areas and perform empirical studies to understand the importance of each to the traceability of software engineering artifacts

Technique Integration for Requirements Assessment

In determining whether to permit a safety-critical software system to be certified and in performing independent verification and validation (IV&V) of safety- or mission-critical systems, the requirements traceability matrix (RTM) delivered by the developer must be assessed for accuracy. The current state of the practice is to perform this work manually, or with the help of general-purpose tools such as word processors and spreadsheets Such work is error-prone and person-power intensive. In this paper, we extend our prior work in application of Information Retrieval (IR) methods for candidate link generation to the problem of RTM accuracy assessment. We build voting committees from five IR methods, and use a variety of voting schemes to accept or reject links from given candidate RTMs. We report on the results of two experiments. In the first experiment, we used 25 candidate RTMs built by human analysts for a small tracing task involving a portion of a NASA scientific instrument specification. In the second experiment, we randomly seeded faults in the RTM for the entire specification. Results of the experiments are presented

Time to Lead on Climate

Timed just prior to the kick-off of this year\u27s UN Climate Conference in Paris and next year\u27s US Presidential and Congressional elections -- and energized by California\u27s growing climate leadership and the Pope\u27s call for global action -- the event poses the increasingly urgent question, How do we fire up the political will to solve Climate Change, and what can each of us do to help

Improving Requirements Tracing via Information Retrieval

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Process improvement for traceability: A study of human fallibility

Abstract—Human analysts working with results from automated traceability tools often make incorrect decisions that lead to lower quality final trace matrices. As the human must vet the results of trace tools for mission- and safety-critical systems, the hopes of developing expedient and accurate tracing procedures lies in understanding how analysts work with trace matrices. This paper describes a study to understand when and why humans make correct and incorrect decisions during tracing tasks through logs of analyst actions. In addition to the traditional measures of recall and precision to describe the accuracy of the results, we introduce and study new measures that focus on analyst work quality: potential recall, sensitivity, and effort distribution. We use these measures to visualize analyst progress towards the final trace matrix, identifying factors that may influence their performance and determining how actual tracing strategies, derived from analyst logs, affect results

Long-Lasting Effects of Prenatal Ethanol Exposure on Fear Learning and Development of the Amygdala

Prenatal ethanol exposure (PrEE) produces developmental abnormalities in brain and behavior that often persist into adulthood. We have previously reported abnormal cortical gene expression, disorganized neural circuitry along with deficits in sensorimotor function and anxiety in our CD-1 murine model of fetal alcohol spectrum disorders, or FASD (El Shawa et al., 2013; Abbott et al., 2016). We have proposed that these phenotypes may underlie learning, memory, and behavioral deficits in humans with FASD. Here, we evaluate the impact of PrEE on fear memory learning, recall and amygdala development at two adult timepoints. PrEE alters learning and memory of aversive stimuli; specifically, PrEE mice, fear conditioned at postnatal day (P) 50, showed deficits in fear acquisition and memory retrieval when tested at P52 and later at P70–P72. Interestingly, this deficit in fear acquisition observed during young adulthood was not present when PrEE mice were conditioned later, at P80. These mice displayed similar levels of fear expression as controls when tested on fear memory recall. To test whether PrEE alters development of brain circuitry associated with fear conditioning and fear memory recall, we histologically examined subdivisions of the amygdala in PrEE and control mice and found long-term effects of PrEE on fear memory circuitry. Thus, results from this study will provide insight on the neurobiological and behavioral effects of PrEE and provide new information on developmental trajectories of brain dysfunction in people prenatally exposed to ethanol

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

The Micro-Orifice Uniform Deposit Impactor–Droplet Freezing Technique (MOUDI-DFT) for Measuring Concentrations of Ice Nucleating Particles as a Function of Size: Improvements and Initial Validation

The micro-orifice uniform deposit impactor– droplet freezing technique (MOUDI-DFT) combines particle collection by inertial impaction (via the MOUDI) and a microscope-based immersion freezing apparatus (the DFT) to measure atmospheric concentrations of ice nucleating particles (INPs) as a function of size and temperature. In the first part of this study we improved upon this recently introduced technique. Using optical microscopy, we investigated the non-uniformity of MOUDI aerosol deposits at spatial resolutions of 1, 0.25 mm, and for some stages when necessary 0.10 mm. The results from these measurements show that at a spatial resolution of 1mm and less, the concentration of particles along the MOUDI aerosol deposits can vary by an order of magnitude or more. Since the total area of a MOUDI aerosol deposit ranges from 425 to 605mm2 and the area analyzed by the DFT is approximately 1.2mm2, this non-uniformity needs to be taken into account when using the MOUDI-DFT to determine atmospheric concentrations of INPs. Measurements of the non-uniformity of the MOUDI aerosol deposits were used to select positions on the deposits that had relatively small variations in particle concentration and to build substrate holders for the different MOUDI stages. These substrate holders improve reproducibility by holding the substrate in the same location for each measurement and ensure that DFT analysis is only performed on substrate regions with relatively small variations in particle concentration. In addition, the deposit non-uniformity was used to determine correction factors that take the non-uniformity into account when determining atmospheric concentrations of INPs. In the second part of this study, the MOUDI-DFT utilizing the new substrate holders was compared to the continuous flow diffusion chamber (CFDC) technique of Colorado State University. The intercomparison was done using INP concentrations found by the two instruments during ambient measurements of continental aerosols. Results from two sampling periods were compared, and the INP concentrations determined by the two techniques agreed within experimental uncertainty. The agreement observed here is commensurate with the level of agreement found in other studies where CFDC results were compared to INP concentrations measured with other methods