Critical Software Research Articles

Deepfakes and documentary practice in an age of misinformation

ABSTRACT The emergence of deepfakes is the latest form to prompt anxieties over the wider implications of misinformation. This chapter explores possibilities for how these technologies extend the repertoire of modalities available for documentary makers. While these ‘synthetic media’ offer a disruption of the documentary genre, they are also a continuation of long-standing trends within software culture and also clearly augment practices which are deeply embedded within the documentary genre. This discussion draws upon Wardle and Derakhshan’s ‘misinformation’ and ‘disinformation’ framework to highlight the increasing complexity of documentary’s forms and the challenges they pose to audiences. The limited experiments in integrating synthetic media into documentary media in a productive way suggest especially the possibilities for using these to develop more openly reflexive content. The proliferation of synthetic media forms prompt a wider need within documentary practitioners for critical data practices, software literacy, and ethical practices embedded within a broader understanding of automated, networked and entangled media systems. And they challenge documentary designers to strategise the nature of their content, and engage more directly with their audiences on questions around evidence, trust, authenticity and the nature of documentary media within an era of misinformation.

Moderator factors of software security and performance verification

Context:Security and performance are critical software non-functional requirements. Therefore, verification activities should be included in the development process to identify related defects, avoiding failures after deployment. However, there is a lack of understanding on factors moderating the security and performance verification, which jeopardizes organizations to improve their verification activities to assure the releasing of software fulfilling these requirements. Objective:To identify moderator factors influencing security and performance verification and actions to promote them. Methods:Case study to identify security and performance moderators factors. Rapid Literature Reviews with Snowballing to strengthen moderator factors confidence. Practitioners Survey to classify the moderator factors relevance. Results:Identification of eight security and performance moderator factors regarding organizational awareness, cross-functional team, suitable requirements, support tools, verification environment, verification methodology, verification planning, and reuse practices. Rapid Reviews confirmed the moderator factors and revealed actions to promote each. A survey with 37 practitioners allowed us to classify the moderator factors and their actions regarding their relevancy. Conclusions:The moderator factors can be considered key points to software development organizations implement/improve security and performance verification activities in regular software systems. Further investigation is necessary to support the understanding of these moderator factors when building modern software systems.

Catala: a programming language for the law

Law at large underpins modern society, codifying and governing many aspects of citizens' daily lives. Oftentimes, law is subject to interpretation, debate and challenges throughout various courts and jurisdictions. But in some other areas, law leaves little room for interpretation, and essentially aims to rigorously describe a computation, a decision procedure or, simply said, an algorithm. Unfortunately, prose remains a woefully inadequate tool for the job. The lack of formalism leaves room for ambiguities; the structure of legal statutes, with many paragraphs and sub-sections spread across multiple pages, makes it hard to compute the intended outcome of the algorithm underlying a given text; and, as with any other piece of poorly-specified critical software, the use of informal, natural language leaves corner cases unaddressed. We introduce Catala, a new programming language that we specifically designed to allow a straightforward and systematic translation of statutory law into an executable implementation. Notably, Catala makes it natural and easy to express the general case / exceptions logic that permeates statutory law. Catala aims to bring together lawyers and programmers through a shared medium, which together they can understand, edit and evolve, bridging a gap that too often results in dramatically incorrect implementations of the law. We have implemented a compiler for Catala, and have proven the correctness of its core compilation steps using the F* proof assistant. We evaluate Catala on several legal texts that are algorithms in disguise, notably section 121 of the US federal income tax and the byzantine French family benefits; in doing so, we uncover a bug in the official implementation of the French benefits. We observe as a consequence of the formalization process that using Catala enables rich interactions between lawyers and programmers, leading to a greater understanding of the original legislative intent, while producing a correct-by-construction executable specification reusable by the greater software ecosystem. Doing so, Catala increases trust in legal institutions, and mitigates the risk of societal damage due to incorrect implementations of the law.

Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM

With the advent of the 4th Industrial Revolution, advanced measurement infrastructure and utilization technologies are being noticeably introduced into the water supply system to store and utilize measurement data. From this perspective, the leak detection technology in water supply networks is becoming increasingly vital to sustainable water resource management and the clean water supply worldwide. In particular, leakage detection of buried pipelines is rated as a very challenging research topic given the current level of technology. However, leakage in buried underground pipelines is rated as a very challenging research topic given the current level of technology. Therefore, a data-driven leak detection model was developed through this study using deep learning technology based on inflow meter data. Multiple threshold-based models were applied to reduce the RNN-LSTM (Recurrent Neural Networks–Long Short-Term Memory models) deep learning and false prediction range, which is programmed in conjunction with the Python language and Google Colaboratory (a big data analysis tool). The developed model consists of flow pattern shape extraction, RNN-LSTM-based flow prediction, and threshold setting modules. The developed model was applied to the actual leakage accident data, followed by the performance evaluation. As a result, the leak was recognized at most points immediately after the accident. The performance of leak detection was evaluated by a Confusion matrix and showed more than 90% accuracy at all points except singularities. Therefore, the developed model can be used as a critical software technology to proactively identify various at present with smart water infrastructure being introduced. In addition, this model is highly scalable as it can consider various operational situations based on the expert system, and it can also efficiently reflect the results of pipe network analysis across different scenarios.

The Predictable Execution Model in Practice

Adoption of multi- and many-core processors in real-time systems has so far been slowed down, if not totally barred, due do the difficulty in providing analytical real-time guarantees on worst-case execution times. The Predictable Execution Model (PREM) has been proposed to solve this problem, but its practical support requires significant code refactoring, a task better suited for a compilation tool chain than human programmers. Implementing a PREM compiler presents significant challenges to conform to PREM requirements, such as guaranteed upper bounds on memory footprint and the generation of efficient schedulable non-preemptive regions. This article presents a comprehensive description on how a PREM compiler can be implemented, based on several years of experience from the community. We provide accumulated insights on how to best balance conformance to real-time requirements and performance and present novel techniques that extend the applicability from simple benchmark suites to real-world applications. We show that code transformed by the PREM compiler enables timing predictable execution on modern commercial off-the-shelf hardware, providing novel insights on how PREM can protect 99.4% of memory accesses on random replacement policy caches at only 16% performance loss on benchmarks from the PolyBench benchmark suite. Finally, we show that the requirements imposed on the programming model are well-aligned with current coding guidelines for timing critical software, promoting easy adoption.

Powerful authentication regime applicable to naval OFP integrated development (PARANOID): a vision for non-circumventable code signing and traceability for embedded avionics software

Purpose This paper aims to describe an effort to provide for a robust and secure software development paradigm intended to support DevSecOps in a naval aviation enterprise (NAE) software support activity (SSA), with said paradigm supporting strong traceability and provability concerning the SSA’s output product, known as an operational flight program (OFP). Through a secure development environment (SDE), each critical software development function performed on said OFP during its development has a corresponding record represented on a blockchain. Design/methodology/approach An SDE is implemented as a virtual machine or container incorporating software development tools that are modified to support blockchain transactions. Each critical software development function, e.g. editing, compiling, linking, generates a blockchain transaction message with associated information embedded in the output of a said function that, together, can be used to prove integrity and support traceability. An attestation process is used to provide proof that the toolchain containing SDE is not subject to unauthorized modification at the time said critical function is performed. Findings Blockchain methods are shown to be a viable approach for supporting exhaustive traceability and strong provability of development system integrity for mission-critical software produced by an NAE SSA for NAE embedded systems software. Practical implications A blockchain-based authentication approach that could be implemented at the OFP point-of-load would provide for fine-grain authentication of all OFP software components, with each component or module having its own proof-of-integrity (including the integrity of the used development tools) over its entire development history. Originality/value Many SSAs have established control procedures for development such as check-out/check-in. This does not prove the SSA output software is secure. For one thing, a build system does not necessarily enforce procedures in a way that is determinable from the output. Furthermore, the SSA toolchain itself could be attacked. The approach described in this paper enforces security policy and embeds information into the output of every development function that can be cross-referenced to blockchain transaction records for provability and traceability that only trusted tools, free from unauthorized modifications, are used in software development. A key original concept of this approach is that it treats assigned developer time as a transferable digital currency.

Time Measurement and Control Blocks for Bare-Metal C++ Applications

Precisely timed execution of resource constrained bare-metal applications is difficult, because the embedded software developer usually has to implement and check the timeliness of the executed application through manual interaction with timers or counters. In the scope of this work, we propose a combined timing specification and concept for time annotation and control blocks in C++. Our proposed blocks can be used to measure and profile software block execution time. Furthermore, it can be used to control and enforce the software time behavior at runtime. After the application of these time blocks, a trace-based verification against the block-based timing specification can be performed to obtain evidence on the correct implementation and usage of the time blocks on the target platform. We have implemented our time block concept in a C++ library and tested it on an ARM Cortex A9 bare-metal platform. The combined usage of timing specification and our time block library has been successfully evaluated on a critical flight-control software for a multi-rotor system.

Analysis of the effect of target-directed treatment based on nutrition-oriented information software on nutritional compliance rate in adults with severe traumatic brain injury: a mixed cohort study

To analyze the effect of target-oriented treatment based on nutrition-oriented information software on nutritional standards of adult patients with severe traumatic brain injury (sTBI). Adult patients with sTBI admitted to the department of emergency intensive care unit (EICU) of Huzhou First People's Hospital were enrolled. Taking the online time of information software as the node on March 1st 2019, the patients who underwent early standardized enteral nutrition (EN) process from March 1st 2018 to February 28th 2019 were taken as the control group. The patients who received nutrition management by the nutritional support management system software for critical patients from March 1st 2019 to February 29th 2020 were used as the experimental group. The software was integrated with critical information system software. The effects of nutritional support in two groups were evaluated, including starting time of EN; total energy supply, total protein supply, energy compliance rate on 7 days and 14 days; the total albumin. And the related indicators of critical illness management were evaluated, including the survival rate of intensive care unit (ICU) at 28 days, duration of invasive mechanical ventilation (IMV), successful rates of weaning from IMV, rapid shallow breath index (RSBI) after spontaneous breathing test (SBT), serum cholinesterase on 7 days and 14 days, etc. Fifty-one patients with sTBI were included in the analysis, 28 in the control group and 23 in the experimental group. There were no significant differences in baseline data between the two groups, such as gender, age, body mass index (BMI), acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, nutritional risk score (NUTRIC), etc., which were comparable. Compared with the control group, the starting time of EN in experimental group was significantly earlier (hours: 26.82±8.33 vs. 36.73±12.86, P = 0.046). The total protein supply on 7 days and 14 days [g×kg-1×d-1: 1.87 (1.36, 1.92) vs. 1.02 (0.87, 1.67), 2.63 (1.49, 1.92) vs. 1.23 (0.89, 1.92), both P < 0.05], the total energy supply on 14 days (kJ×kg-1×d-1: 154.26±68.16 vs. 117.99±112.42, P = 0.033), the energy compliance rate on 14 days [80.0% (16/20) vs. 35.7% (10/28), P = 0.002], and the serum cholinesterase on 14 days [U/L: 5 792.5 (4 621.0, 8 131.0) vs. 4 689.7 (3 639.0, 7 892.0), P = 0.048] in experimental group were significantly increased. There were no significant differences in other indicators between the two groups [total energy supply on 7 days (kJ×kg-1×d-1): 91.50±30.50 vs. 92.88±28.16, P = 0.184; energy compliance rate on 7 days: 34.7% (8/23) vs. 21.4% (6/28), P = 0.288; total albumin (g): 97.80±46.29 vs. 114.29±52.68, P = 0.086; 28-day survival rate of ICU: 87.0% vs. 78.6%, P = 0.081; duration of IMV (days): 14.33±7.68 vs. 15.68±6.82, P = 0.074; successful rates of weaning from IMV: 69.6% vs. 67.9%, P = 0.895; RSBI after SBT (breaths×min-1×L-1): 26.84±10.69 vs. 33.68±8.94, P = 0.052; serum cholinesterase on 7 days (U/L): 4 289.7 (2 868.0, 7 291.0) vs. 3 762.2 (2 434.0, 6 892.0), P = 0.078]. The development and clinical application of nutrition support information software is helpful for the standardized implementation of the nutritional support treatment process for adult patients with sTBI, which is worthy of further clinical research and promotion.

Avoidance behaviour towards using pirated software: testing a seven-component model on SME employees

PurposeThis research aims to develop and test a conceptual model for shaping small and medium enterprise (SME) employees' avoidance behaviour towards using pirated software. The model specifies the components of morality, spirituality, emotional intelligence and ethical values that influence employees' avoidance behaviour towards using pirated software.Design/methodology/approachA conceptual framework was developed and tested on the basis of information technology and management literature by using data from 275 influential and active employees of SMEs. Data were collected via a survey and analysed through covariance-based structural equation modeling (CB-SEM).FindingsIn the context of SMEs, employees' avoidance behaviour towards using pirated software is motivated by significant moral antecedents. Moral equity and judgement significantly influence employees' avoidance behaviour towards using pirated software when moral emotion mediates such a relationship. In addition, individual spirituality significantly moderates the relationship between moral equity and moral emotion. Employees' emotional intelligence optimises the strength of the relationship between moral judgement and moral emotion. Employees' likelihood to engage in unethical behaviour decreases when they exhibit strong ethical values in the relationship between moral emotion and their behaviour towards using pirated software.Research limitations/implicationsThis study offers theoretical support for employees' avoidance behaviour towards using pirated software. The findings of this cross-sectional work have limited generalisability. Single-country data may not be generalised to SME employees in other countries. Thus, cross-country analysis and additional measures and antecedents must be developed and identified in the future.Practical implicationsPolicymakers and managers should consciously review the proposed seven-component model that causes SME employees to avoid the use of pirated software. Ethical standards that lessen the use of pirated software can be improved if managers and policymakers understand the components of moral equity and judgement that influence moral emotions.Originality/valueThis study is the first to examine the specific antecedents of the ethical standards and avoidance behaviours of SME employees towards the use of pirated software. As such, it provides a foundation for further studies on this critical area and software piracy in the context of SMEs in an emerging economy, which is limited in current literature.

Development of strategic maintenance prediction model for critical equipment maintenance

Abstract Manufacturing industries deploy a number of equipment which are functionally critical to the daily operations of the system and as such requires effective maintenance, and monitoring for high productivity to be achieved. Though maintenance of facilities has gained impressive recognition in many industries, the implementation of inappropriate maintenance practices has increased level of frustrations due to little result from huge financial commitment to maintenance in many production systems. In this work a programmable method was developed for the determination of appropriate maintenance strategy for critical machine in a manufacturing company. The system tagged C3MPS (critical machine maintenance management program software) was developed on the strength of Parallel Criteria Model (PCM), Equipment Criticality Rating (ECR) and Analytic Hierarchy Process (AHP) as subroutines for the establishment of effective maintenance practice. An application of the resulting software in a plastic production company suggested a predictive maintenance as the most effective compared to the existing strategies in practice by the company. The software developed has capacity to effectively evaluate the criticality of equipment and maintenance practice.

Critically Analyzing the Online Classroom: Blackboard, Moodle, Canvas, and the Pedagogy They Produce

Working from the crossroads of critical pedagogy and software studies, this study analyzes the means by which teaching technologies—in particular the popular learning management systems (LMS) Blackboard, Moodle, and Canvas—support a transmission model of education at the expense of critical learning goals. I assess the effect of LMSs on critical aims via four key critical pedagogy concepts: the banking system, student/teacher contradiction, dialogue, and problem-posing. From software studies, I employ the notion of affordances—what program functions are and are not made available to users—to observe how LMSs naturalize the transmission model. Rather than present a deterministic look at teaching technology, this study calls for closer examination of these tools in order to rework teaching technologies toward critical ends.

Case-based reasoning for safety assessment of critical software

The commissioning of a new guided or automated rail transport system requires an in-depth analysis of all the methods, techniques, procedures, regulations and safety standards to ensure that the risk level of the future system does not present any danger likely to jeopardize the safety of travelers. Among these numerous safety methods implemented to guarantee safety at the system, automation, hardware and software level, there is a method called “Software Errors and Effects Analysis (SEEA)” whose objective is to determine the nature and the severity of the consequences of software failures, to propose measures to detect errors and finally to improve the robustness of the software. In order to strengthen and rationalize this SEEA method, we have agreed to use machine learning techniques and in particular Case-Based Reasoning (CBR) in order to assist the certification experts in their difficult task of assessing completeness and the consistency of safety of critical software equipment. The main objective consists, from a set of data in the form of accident scenarios or incidents experienced on rail transport systems (experience feedback), to exploit by automatic learning this mass of data to stimulate the imagination of certification experts and assist them in their crucial task of researching scenarios of potential accidents not taken into account during the design phase of new critical software. The originality of the tool developed lies not only in its ability to model, capitalize, sustain and disseminate SEEA expertise, but it represents the first research on the application of CBR to SEEA. In fact, in the field of rail transport, there are currently no software tools for assisting SEEAs based on machine learning techniques and in particular based on CBR.

Survey on software defect prediction techniques

ABSTRACT Recent advancements in technology have emerged the requirements of hardware and software applications. Along with this technical growth, software industries also have faced drastic growth in the demand of software for several applications. For any software industry, developing good quality software and maintaining its eminence for user end is considered as most important task for software industrial growth. In order to achieve this, software engineering plays an important role for software industries. Software applications are developed with the help of computer programming where codes are written for desired task. Generally, these codes contain some faulty instances which may lead to the buggy software development cause due to software defects. In the field of software engineering, software defect prediction is considered as most important task which can be used for maintaining the quality of software. Defect prediction results provide the list of defect-prone source code artefacts so that quality assurance team scan effectively allocate limited resources for validating software products by putting more effort on the defect-prone source code. As the size of software projects becomes larger, defect prediction techniques will play an important role to support developers as well as to speed up time to market with more reliable software products. One of the most exhaustive and pricey part of embedded software development is consider as the process of finding and fixing the defects. Due to complex infrastructure, magnitude, cost and time limitations, monitoring and fulfilling the quality is a big challenge, especially in automotive embedded systems. However, meeting the superior product quality and reliability is mandatory. Hence, higher importance is given to V&V (Verification & Validation). Software testing is an integral part of software V&V, which is focused on promising accurate functionality and long-term reliability of software systems. Simultaneously, software testing requires much effort, cost, infrastructure and expertise as the development. The costs and efforts elevate in safety critical software systems. Therefore, it is essential to have a good testing strategy for any industry with high software development costs. In this work, we are planning to develop an efficient approach for software defect prediction by using soft computing based machine learning techniques which helps to predict optimize the features and efficiently learn the features.

Optimisation of the use of sliding window deconvolution for comprehensive characterisation of trastuzumab and adalimumab charge variants by native high resolution mass spectrometry

The biopharmaceutical industry continues to develop mAb-based biotherapeutics in increasing numbers. Due to their complexity, there are several critical quality attributes (CQAs) that need to be measured and controlled to guarantee product safety and efficacy. Charge variant analysis is a widely used method to monitor changes in product quality during the manufacturing process of monoclonal antibodies (mAbs) and, together with a bottom-up peptide centred approach, acts as a key analytical platform to fulfil regulatory requirements. Native MS measures biomolecules under conditions that preserve most aspects of protein tertiary and quaternary structure, enabling direct characterization of large intact proteins such as mAbs. The resulting native mass spectrum of a mAb is characterized by a narrower charge-state envelope that simplifies the spectra and also condenses the ion signals into fewer peaks, increasing the signal-to-noise ratio. Algorithmic spectral deconvolution is needed for routine accurate and rapid molecular weight determination, and consequently, multiple deconvolution algorithms have evolved over the past decade. Here, we demonstrate the utility of the sliding window algorithm as a robust and powerful deconvolution tool for comprehensive characterisation of charge variant analysis data for mAbs. Optimum performance is evaluated by studying the impact of critical software parameters on detection, identification and relative quantitation of protein isoforms. By combining molecular mass and retention time information, it was possible to identify multiple modifications on adalimumab and trastuzumab, both IgG1 mAbs, including lysine truncation, deamidation and succinimide formation, along with the N-glycan distribution of each of the identified charge variants. Sliding window deconvolution also provides a key benefit of low abundant variant detection in a single analysis and the ability to detect co-eluting components with different relative abundances. The studied mAbs demonstrate the algoritms applicability for efficient data processing of both simple and complex mAbs analysed using pH gradient cation exchange chromatography coupled to native mass spectrometry.

Application of high-level methods of compromise optimization for control of autonomous robotized open pit mining

In most software engineering approaches, software design begins with defining functional requirements, which is well suited to web-based software development projects. When designing high-critical large-scale software intended for industrial use, accounting for non-functional software requirements is also required. The main idea of the proposed document-oriented approach is to design a stable architectural solution as early as possible, taking into account the nonfunctional characteristics of the software: reliability, security, maintainability and performance (quality attributes). At the same time, the key issue is the coordination of functional requirements, taking into account technical limitations and business requirements achieved during the steady interaction of customer and developer teams. To increase the flexibility of the designed solutions and prevent crisis situations when developing highly critical large-scale software, it is proposed to use the approach integrating the architecture-centric design method (ACDM), the architecture-tradeoff analysis method (ATAM) with a matrix enterprise architecture matrix (EAM). This allows getting a result that is adequate to the required level of responsibility and reliability. Consideration of quality attributes within the framework of the method of compromise analysis makes it possible to select and make certain decisions in software design taking into account the scale of the software and its scope. The main attributes of product quality are highlighted (ISO 25010 standard), critical scenarios are defined for each of them (templates and use cases). The use of these templates for detailed software design with the necessary parameters of functional requirements, business conditions and technological limitations reduces the risk of developing unpredictable and uncertain system behavior. Based on the proposed approach, an architectural solution is presented for highly critical, responsible, large-scale software for managing autonomous robotic open-pit mining of minerals. Critical attributes for creating the specified software were identified and ranked, and the architecture of the solution according to the SWEBOK software development standard was described. Further, taking into account the nature, scale and scope of the software solution, recommendations are given on high-level architectural templates for the system design, including layers, pipelines and microservices. The proposed architecture-oriented development method is suitable for industrial-level software in various subject areas.

SUBVERSIVE IMAGINATION AND POSSIBILITIES OF CRITICAL AGENCY IN THE LANDSCAPES OF CODE?

This paper reflects the intertwinements of ‘agency’, ‘infrastructuration’, and ‘imagination’ in our increasingly networked technological everyday life. The focus of the research, navigating at the interfaces of critical media studies, domestication theory, STS, and critical software studies, is on the ways in which imaginations on agency are constructed and stabilized. Likewise, attention is directed to how societal power structures are produced, reproduced, and possibly challenged in the processes of constructing imaginaries of agency. To study the above mentioned perspectives together, critical cultural studies scholar Stuart Hall’s famous encoding/decoding model is applied and updated in the contemporary media technological context (Hall, 1973; 1980). Hall’s concept of ‘maps of meaning’ is brought in dialogue with the concepts of ‘sociotechnical imaginaries’ by Sheila Jasanoff and ‘social imaginaries’ by Charles Taylor. The multidisciplinary, multimethod, and multidata approach of the research sheds light on the diversity and complexity of imaginaries of agency in our times. Based on the results, it is suggested that there prevails an almost resigned sense of agency that many people share in relation to the conditions of their technologically mediated everyday life. Despite sporadic negotiations and dissonances, it seems that people have become accustomed to the idea that they have very little, if any, chances to influence the structures of their networked daily environments. It is suggested that future research should concentrate on making visible alternatives to modes of technology-related action as well as develop ways to challenge people to creatively (re)imagine the kind of technology they want to live with.

Ruggedness testing of liquid chromatography-tandem mass spectrometry system components using microbiome-relevant methods and matrices

Recently, an opportunity to perform a broad ruggedness assessment of our liquid chromatography-tandem mass spectrometry (LC-MS/MS) system presented itself during the analytical planning phase of a large-scale human fecal microbiome study. The specific aim of this project was to study the microbial-mediated metabolism of a targeted set of bile acids/salts by mixed bacterial communities cultured from the feces of 12 healthy volunteers when grown in a custom growth medium and following exposure to different clinically-relevant antibiotics. The magnitude of this study offered a rare opportunity to significantly stress procedures and LC-MS/MS system components comprised in our bile acid/salt targeted metabolomics method. With this second specific aim in mind, we modified the sample analysis plan to include a series of figure-of-merit (FoM)-based tests that are commonly used in regulated bioanalytical labs to assess LC and MS system ruggedness for a specific assay – these FoM-based testing parameters were monitored continuously over the course of sample analysis and the results are presented in this report. In total, the assessment included 1206 sequential injections (180 calibration standards, 136 blank-internal standard samples, and 890 diluted medium samples) that took place over 8-days. Completion of the 8-days of non-stop sample analysis revealed no critical hardware or software failures, and the analysis of the FoM-based tests indicated no observable degradation of system performance over the number of samples and time tested. The FoM-based test metrics presented may be used as a template to assess the ruggedness of any LC-MS/MS-based targeted metabolomics workflow.

Application of Case-Based Reasoning to the safety assessment of critical software used in rail transport

The risk assessment of railway accidents requires the implementation of several safety analysis methods such as Preliminary Hazard Analysis, Functional Safety Analysis, Analysis of Failure Modes, their Effects and their Criticality and Software Error Effect Analysis (SEEA). The study proposed within the framework of this article concerns the SEEA method whose objective is to determine the nature and severity of the consequences of software failures, to propose measures to detect errors and improve the robustness of the software. The goal is to develop a new approach to analysis and evaluation of the safety of critical software, based on machine learning and more precisely on the Case-Based Reasoning (CBR). The approach adopted involves two main activities: The first step in acquiring safety knowledge consists in extracting, modeling and archiving dangerous situations to produce a library of standard cases which covers the entire problem. The second stage of machine learning is to exploit historical knowledge (experience feedback) in order to assist safety experts in their critical task of analyzing and assessing the safety of software involved in guided or automated rail transport systems. This second activity involves the use of Case-Based Reasoning (CBR).

Passages

Federico Biancuzzi and Shane Warden's Masterminds of Programming: Conversations with the Creators of Major Programming Languages is a treasure. The book consists of interviews with the creators of, in order, C++, Python, APL, Forth, BASIC, AWK, Lua, Haskell, ML, SQL, Objective C, Java, C#, UML, Perl, PostScript, and Eiffel. Each chapter asks similar, but not identical questions, and the above-mentioned masterminds, including Larry Wall, James Gosling, Brian Kernighan, Bertrand Meyer, Robin Milner, Simon Peyton-Jones, Guido van Rossum, and Bjarne Stroustrop give a wide variety of answers. Some of the masterminds are charming, and many are contentious, even cranky; they are also, almost all, full of deep insights into the deepest problems of software engineering. This insight comes in two forms; first, programming languages are the mechanisms by which software engineering solutions are almost always produced. Second, perhaps even more importantly, creating and evolving a widely-used programming language is a heroic, Herculean, critical software engineering task. All of these masterminds have succeeded in a massive software engineering task; they are not mere ivory tower thinkers about software engineering, but have, in some cases, entire lives shaped by a single, extremely complex, software project. More on that key point below.

Test-Driven Approach for Safety-Critical Software Development

Nowadays software control large majority of systems that humankind use. Systems that software is used widely, such as transportation, military, medicine and avionics must be safe during operation. Fail in these critical systems may cause catastrophic results (i.e. loss of human life, loss or severe damage to environment or equipment etc.). In order to avoid failure on safety critical systems or at least mitigate risks detailed testing is required. Many safety critical systems are developed with sequential phases and tested with test last approach. But test last approach is not sufficient when requirements are unclear or changed. To overcome weaknesses of test last approach we propose test driven approach for safety critical software development, regulated by IEC 61508 standard. A systematic analysis of IEC 61508 software requirements against proposed approach showed that the approach directly supports some objectives and partially supports some of them. Supported objectives are safety requirement identification, simple and testable software design, verification and validation. In general introduced approach suits regulated software development well and this paper outlines these details.