Method for determining the number of defect-free tests of software for an automated system of preparing unmanned aerial vehicles flight data
Authors: Kazakov G.V., Koryanov V.V.
Published in issue: #6(126)/2022
DOI: 10.18698/2308-6033-2022-6-2188
Category: Aviation and Rocket-Space Engineering | Chapter: Aircraft Dynamics, Ballistics, Motion Control
Calculating the reliability indicator of software for an automated system of preparing unmanned aerial vehicles flight data is special because of the variety of permitted points of the vehicles departure, one of which cannot be determined in advance. Since data quality control requires significant time resources, it is necessary to define a criterion according to which the entire amount of data is either accepted or rejected, or tests continue. Existing methods for assessing the software reliability index have shortcomings that prevent methods to be practically used in assessing the flight data quality. In this regard, we developed a new method based on monitoring the selection of test variants of input data from the permissible region using the results of syntactic and semantic control of the minimum required amount of test variants. The method assesses the quality indicator of the flight data of unmanned aerial vehicles with a given probability of an error of the second kind and is easily implemented in practice. For its implementation, we give the concept of an auxiliary sampling plan and prove a lemma on the uniqueness of this plan and theorems on the existence of quasi-optimal sampling plans according to the criterion of the minimum dead zone for making a positive decision with two and one negative test outcomes, as well as a defect-free plan. We also developed decisive rules for making a positive or negative decision based on the results of software tests.
References
[1] Morris W. Management Science: A Bayesian Introduction. 1st ed. Prentice-Hall, Englewood Cliffs, NJ, 1968 [In Russ.: Morris W. Nauka ob upravlenii: bayesovskiy podkhod. Moscow, Mir, 1971, 304 p.].
[2] Gmurman V.E. Teoriya veroyatnostey i matematicheskaya statistika [Fundamentals of Probability Theory and Mathematical Statistics]. Moscow, Urait Publ., 2016, 479 p.
[3] Bernardo J.M., Smith A.F.M. Bayesian Theory. Wiley series in probability and statistics. Wiley & Sons, Ltd, 2000, 611 p.
[4] Congdon P. Bayesian statistical modelling. 2nd ed. Wiley series in probability and statistics. Wiley & Sons, Ltd, 2006, 598 p.
[5] Goncharov R.B. Izvestiya vysshikh uchebnykh zavedeniy. Mashinostroenie — BMSTU Journal of Mechanical Engineering, 2019, no. 4, pp. 28–40. DOI: 10.18698/0536-1044-2019-4-28-40
[6] Timofeev V.S., Faddeenkov A.V. Nauchny vestnik NGTU — Science Bulletin of the NSTU, 2015, vol. 60, no. 3, pp. 58–68.
[7] Andreev A.G., Kazakov G.V., Koryanov V.V. Inzhenerny zhurnal: nauka i innovatsii — Engineering Journal: Science and Innovation, 2016, iss. 6. http://dx.doi.org/10.18698/2308-6033-2016-06-1504
[8] Draper H. Applied regression analysis. 3rd ed. Wiley-Interscience, 1998, 736 p. [In Russ.: Draper H. Prikladnoy regressionny analiz. Moscow, Williams Publ., 2019, 912 p.].
[9] Kutner M.H., Nachtsheim C.J., Neter J., Li W. Applied Linear Statistical Models. McGraw-Hill, 2004, 1424 p.
[10] Wald A. Sequential analysis. Dover Publications, 2013, 224 p. [In Russ.: Wald A. Posledovatelny analiz. Moscow, Fizmatgiz Publ., 1960, 328 p.].
[11] Baulina E.E., Dementev Yu.V., Krutashov A.V., Serebryakov V.V., Deev O.I., Filonov A.I. Izvestiya vysshikh uchebnykh zavedeniy. Mashinostroenie — BMSTU Journal of Mechanical Engineering, 2016, no. 6, pp. 42–49.
[12] Postovalov S.N. Doklady AN VSh RF — Proceedings of the Russian Higher School Academy of Sciences, 2011, no. 2, pp. 140–150.
[13] Oleynikova S.A., Kirilov A.A. Vestnik Voronezhskogo gos. tekhn. un-ta (VSTU Proceedings), 2011, no. 7, pp. 209–212.
[14] Shvedov A.S. Beta-raspredelenie sluchaynoy matritsy i ego primenenie v modeli sostoyanie-nablyudenie [Beta distribution of a random matrix and its application in the state-observation model]. Preprint WP2/2009/01. Moscow, HSE Publ., 2009, 36 p.
[15] Sudakov R.S., et al. Otsenka nadezhnosti izdelii pri konstruktorskikh ispytaniyakh s uchetom provodimykh dorabotok [Evaluation of the reliability of products during design tests, taking into account ongoing improvements]. Leningrad, LDNTP Publ., 1979, pp. 18–23.
[16] Timofeev G.A., Barbashov N.N., Terenteva A.D. Izvestiya vysshikh uchebnykh zavedeniy. Mashinostroenie — BMSTU Journal of Mechanical Engineering, 2016, no. 12, pp. 58–65.
[17] Titova L.A. Ekonominfo (Economicinfo), 2009, no. 12, pp. 28–32.
[18] Yudin S.V. Fundamentalnye issledovaniya — Fundamental research, 2015, no. 10 (part 2), pp. 324–329.
[19] Sazhin Yu.V., Basova V.A., Egorova G.V. Statisticheskie metody analiza i kontrolya kachestva produktsii [Statistical methods of analysis and quality control of products]. Monograph. Tolyatti, TGIS Publ., 2003, 246 p.
[20] Grudinin V.G. Vestnik Irkutskogo Gosudarstvennogo Tekhnicheskogo Universiteta — The Bulletin of Irkutsk State University, 2013, no. 5, pp. 25–32.
[21] Markelov V.V., Vlasov A.I., Zoteva D.E. Nadezhnost i kachestvo slozhnykh system — Reliability and Quality of Complex Systems, 2015, no. 1, pp. 58–62.
[22] Dzirkal E.V. Nadezhnost — Dependability, 2014, no. 3, pp. 137–143.
[23] GOST R 50779.81–2018. Statisticheskie metody. Dvukhstupenchatye plany kontrolya po alternativnomu priznaku s minimalnym obemom vyrabotki na osnove znachenii PRQ I CRQ [GOST R 50779.81–2018. Statistical methods. Two-stage attribute control plans with minimum production based on PRQ and CRQ]. Moscow, Standartinform Publ., 2018, 78 p.