Enabling Traceability Reuse for Impact Analyses - Toward a Recommendation System in a Safety Context

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Markus Borg

Converted Prezi explaining recommendation systems in general and our initial work with ImpRec.

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Enabling Traceability Reuse for Impact Analyses - Toward a Recommendation System in a Safety Context

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Recommendation Systems for Issue Management

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Building trust with partners and trust to get things done. Building relationships with partners and internally is central in terms of being able to command and conquer in fast growing companies, especially when you grow from 3 to 800 employees (from 0 to 1.5 billion SEK revenues )in 7 years. That trust has two sides: how to accomplish a strong relationship with a partner and even so importantly how to build a solid base internally to accomplish predictability and make sure that teams work towards common goals?

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Nine Oakland University students made a difference by volunteering their time to raise money and collect food for Baldwin Center visitors. Dishing Out to the "D" raised $430 after hosting three fundraising events and spent six hours at the Baldwin Center on two occassions serving soup to the less fortunate, organizing the church, and putting food and clothes into boxes. To find out more about Dishing Out to the "D," please contact Corinna Muntean at munteancorinna@gmail.com.

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Conference presentation from CSMR 2013, Genova, Italy. Abstract: Completely analyzed and closed issue reports in software development projects, particularly in the development of safety-critical systems, often carry important information about issue-related change locations. These locations may be in the source code, as well as traces to test cases affected by the issue, and related design and requirements documents. In order to help developers analyze new issues, knowledge about issue clones and duplicates, as well as other relations between the new issue and existing issue reports would be useful. This paper analyses, in an exploratory study, issue reports contained in two Issue Management Systems (IMS) containing approximately 20.000 issue reports. The purpose of the analysis is to gain a better understanding of relationships between issue reports in IMSs. We found that link-mining explicit references can reveal complex networks of issue reports. Furthermore, we found that textual similarity analysis might have the potential to complement the explicitly signaled links by recommending additional relations. In line with work in other fields, links between software artifacts have a potential to improve search and navigation in large software engineering projects.

Analyzing networks of issue reports

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Conference presentation from ENASE 2012 in Wroclaw, Poland. Abstract: Since software development is of dynamic nature, the impact analysis is an inevitable work task. Traceability is known as one factor that supports this task, and several researchers have proposed traceability recovery tools to propose trace links in an existing system. However, these semi-automatic tools have not yet proven useful in industrial applications. Based on an established automation model, we analyzed the potential value of such a tool. We based our analysis on a pilot case study of an impact analysis process in a safety-critical development context, and argue that traceability recovery should be considered an investment in ndability. Moreover, several risks involved in an increased level of impact analysis automation are already plaguing the state- of-practice workflow. Consequently, deploying a traceability recovery tool involves a lower degree of change than has previously been acknowledged.

Findability through Traceability - A Realistic Application of Candidate Tr...

Markus Borg

Issue management is a costly part of software development. In large projects, the continuous inflow of issue reports contributes to the information overload in a project, i.e., "a state where individuals do not have time or capacity to process all available information". In issue triaging, an initial step in issue management, a developer must be able to overview existing issue reports and easily navigate the software engineering project landscape. In this presentation, we present support for two work tasks involved in issue management: 1) issue assignment and 2) change impact analysis. We use machine learning to harness the ever-growing number of issue reports, by training recommendation systems on previous issues. Our industrial evaluations on 50,000+ issue reports in two large software development organizations indicate that automated issue assignment performs in line with current manual work. Moreover, we present how traceability from already resolved issue reports to various artifacts can be reused to jump start change impact analyses for newly submitted issues. Finally, we speculate on future ways to tame information overload into helpful software engineering recommendations.

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Recommendation Systems for Issue Management

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Findability through Traceability - A Realistic Application of Candidate Tr...

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Testing Quality Requirements of a System-of-Systems in the Public Sector - Ch...

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Plus de Markus Borg

Keynote at the 6th Int’l. Conference on Lean and Agile Software Development, January 22, 2022 Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined "Software 2.0," but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.

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Presented at CAIN22 - 1st Conference on AI Engineering – Software Engineering for AI Due to the migration megatrend, efficient and effective second- language acquisition is vital. One proposed solution involves AI- enabled conversational agents for person-centered interactive language practice. We present results from ongoing action research targeting quality assurance of proprietary generative dialog models trained for virtual job interviews. The action team elicited a set of 38 requirements for which we designed corresponding automated test cases for 15 of particular interest to the evolving solution. Our results show that six of the test case designs can detect meaningful differences between candidate models. While quality assurance of natural language processing applications is complex, we provide initial steps toward an automated framework for machine learning model selection in the context of an evolving conversational agent. Future work will focus on model selection in an MLOps setting.

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Machine Learning (ML) is a fundamental part of modern perception systems. In the last decade, the performance of computer vision using trained deep neural networks has outperformed previous approaches based on careful feature engineering. However, the opaqueness of large ML models is a substantial impediment for critical applications such as in the automotive context. As a remedy, Gradient-weighted Class Activation Mapping (Grad-CAM) has been proposed to provide visual explanations of model internals. In this paper, we demonstrate how Grad-CAM heatmaps can be used to increase the explainability of an image recognition model trained for a pedestrian underpass. We argue how the heatmaps support compliance to the EU's seven key requirements for Trustworthy AI. Finally, we propose adding automated heatmap analysis as a pipe segment in an MLOps pipeline. We believe that such a building block can be used to automatically detect if a trained ML-model is activated based on invalid pixels in test images, suggesting biased models.

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The increasing levels of software- and data-intensive driving automation call for an evolution of automotive software testing. As a recommended practice of the Verification and Validation (V&V) process of ISO/PAS 21448, a candidate standard for safety of the intended functionality for road vehicles, simulation-based testing has the potential to reduce both risks and costs. There is a growing body of research on devising test automation techniques using simulators for Advanced Driver-Assistance Systems (ADAS). However, how similar are the results if the same test scenarios are executed in different simulators? We conduct a replication study of applying a Search-Based Software Testing (SBST) solution to a real-world ADAS (PeVi, a pedestrian vision detection system) using two different commercial simulators, namely, TASS/Siemens PreScan and ESI Pro-SiVIC. Based on a minimalistic scene, we compare critical test scenarios generated using our SBST solution in these two simulators. We show that SBST can be used to effectively and efficiently generate critical test scenarios in both simulators, and the test results obtained from the two simulators can reveal several weaknesses of the ADAS under test. However, executing the same test scenarios in the two simulators leads to notable differences in the details of the test outputs, in particular, related to (1) safety violations revealed by tests, and (2) dynamics of cars and pedestrians. Based on our findings, we recommend future V&V plans to include multiple simulators to support robust simulation-based testing and to base test objectives on measures that are less dependant on the internals of the simulators.

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While Deep Neural Networks (DNN) have revolutionized applications that rely on computer vision, their characteristics introduce substantial challenges to automotive safety engineering. The behavior of a DNN is not explicitly expressed by an engineer in source code, instead enormous amounts of annotated data are used to learn a mapping between input and output. Functional safety as defined by ISO 26262 is not sufficient to match the needs for the new generation of data-driven software. Earlier this year, ISO/PAS 21148 Safety of the Intended Functionality (SOTIF) was published by ISO. SOTIF is a Publicly Available Specification (PAS), a response to a pressing need of an automotive safety standard appropriate for machine learning. A PAS is a stepping stone toward a new ISO standard, and SOTIF is intended to complement conventional functional safety as defined in ISO 26262. In this presentation, we introduce the SOTIF process and present our contributions on how to support safety of the intended function. First, we present search-based software testing to efficiently and effectively idenify test scenarios that cause safety violations in simulated environments. Second, we present a safety cage architecture that helps percepiont systems reject input that does not resemble the training data.

Trained, Not Coded - Still Safe?

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Numerous empirical software engineering studies rely on detailed information about bugs. While issue trackers often contain information about when bugs were fixed, details about when they were introduced to the system are often absent. As a remedy, researchers often rely on the SZZ algorithm as a heuristic approach to identify bug-introducing software changes. Unfortunately, as reported in a recent systematic literature review, few researchers have made their SZZ implementations publicly available. Consequently, there is a risk that research effort is wasted as new projects based on SZZ output need to initially reimplement the approach. Furthermore, there is a risk that newly developed (closed source) SZZ implementations have not been properly tested, thus conducting research based on their output might introduce threats to validity. We present SZZ Unleashed, an open implementation of the SZZ algorithm for git repositories. This paper describes our implementation along with a usage example for the Jenkins project, and conclude with an illustrative study on just-in-time bug prediction. We hope to continue evolving SZZ Unleashed on GitHub, and warmly invite the community to contribute.

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Cotton crops are vulnerable to a variety of sucking pests, which can severely impact plant health, yield, and fiber quality. These pests primarily feed on plant sap, extracting nutrients directly from the plant's vascular system. Here's a breakdown of some of the most significant sucking pests in cotton cultivation: Aphids: Cotton aphids or melon aphids can cause direct damage by sucking sap and indirect damage by secreting honeydew, which encourages sooty mold growth. This can interfere with photosynthesis and weaken the plant. Aphids can also transmit viral diseases. Whiteflies: Two species, the silverleaf whitefly and the bandedwinged whitefly, are particularly troublesome. They not only suck sap from the underside of leaves, causing yellowing and leaf drop, but their honeydew excretion promotes sooty mold and they can transmit several plant viruses. Thrips: While thrips can chew on plants, their primary damage to cotton is through sucking. They attack the cotton plant during its seedling stage, which can stunt growth and reduce vigor. Thrips are also capable of transmitting the Cotton Bud disease. Spider Mites: These are not insects but arachnids. Spider mites, such as the two-spotted spider mite, suck cell contents from the leaves, leading to speckled discoloration and potentially significant leaf loss if infestations are severe. Leafhoppers: Including various species, leafhoppers can cause direct damage through feeding, which results in leaf curling and stunted growth. They can also be vectors for plant diseases. Mealybugs: These pests are less common but can be problematic, especially in clustered planting conditions. They suck sap and secrete honeydew, which leads to sooty mold. Mealybugs can also spread viruses. Stink Bugs: Although primarily known for their chewing mouthparts, certain stink bugs can cause damage similar to sucking pests by injecting saliva into the plant and sucking out nutrients, leading to boll damage and stained lint. Management Strategies: Cultural Controls: This includes practices such as crop rotation, using resistant varieties, and managing planting and harvesting times to avoid peak pest populations. Biological Controls: Beneficial insects like lady beetles, lacewings, and predatory mites can naturally control sucking pest populations. Parasitic wasps also play a role in controlling aphid and whitefly populations. Chemical Controls: Insecticides may be used but should be chosen carefully to minimize resistance development and preserve beneficial insects. Systemic insecticides can be particularly effective against sucking pests. Integrated Pest Management (IPM): Combining multiple control strategies based on monitoring and thresholds to apply the most effective and environmentally sensitive approach. Effective management of sucking pests in cotton requires a thorough understanding of the pest species present, their life cycles, and the ecological balance of the field environment.

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Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

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Mustard, as a crop, is susceptible to a variety of pests that can affect its growth and yield. Here’s a rundown of some common pests that target mustard plants: Aphids: These small, sap-sucking insects can cause significant damage by feeding on the leaves and stems. Aphids also excrete a sticky substance known as honeydew, which can lead to the growth of sooty mold on the plants. Flea Beetles: These tiny beetles jump like fleas when disturbed and chew small holes in the leaves. They are particularly damaging in the early growth stages of the plant. Cabbage Loopers: The larvae of a type of moth, these caterpillars are known for their distinctive looping movement. They chew large holes in the leaves and can defoliate plants if present in large numbers. Diamondback Moth Larvae: Another caterpillar pest, these larvae chew small holes in the leaves and can cause extensive damage, especially when infestations are heavy. Whiteflies: These are tiny, winged insects that feed on plant sap and can quickly become a problem in greenhouse or close planting conditions. Like aphids, they also secrete honeydew. Cutworms: These are the larvae of certain types of moths and are known for cutting young plants at the stem base at ground level. They are most destructive during the night. Root Maggots: The larvae of root maggot flies, these pests attack the roots of mustard plants, causing wilting and potentially killing young plants. Harlequin Bugs: These are colorful stink bugs that suck the sap from mustard plant stems and leaves, causing the leaves to become stippled, wilt, and eventually die if the infestation is severe. Mustard Sawfly: The larvae of the mustard sawfly can cause considerable defoliation, as they feed voraciously on the leaves. Clubroot: Caused by a fungus-like organism, clubroot affects the roots, causing them to swell and distort. While technically a disease, it is often associated with pest management because controlling it involves similar preventative strategies. Control Measures: Managing pests in mustard involves a combination of cultural, biological, and chemical methods. Crop rotation, resistant varieties, timely sowing, maintaining plant health, and using natural predators like ladybugs and parasitic wasps can help keep pest populations under control. Chemical pesticides should be used as a last resort due to their potential impact on the environment and non-target species.

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